ACL Wiki - User contributions [en]

Temporal Information Extraction (State of the art)

2015-06-10T03:45:38Z

Bethard: Adds references for Clinical TempEval papers

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Task A: Temporal expression extraction and normalisation====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| <ref name="Stroetgen-2013">Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.</ref>
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| <ref name="Chambers-2013">Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| <ref name="Filannino-2013">Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.</ref>
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| <ref name="Chang-2013">Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| <ref name="Jung-2013">Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.</ref>
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| <ref name="Bethard-2013">Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.</ref>
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| <ref name="Kolya-2013">Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.</ref>
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| <ref name="Kolomiyets-2013">Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.</ref>
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| <ref name="Zavarella-2013">Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.</ref>
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| <ref name="Jung-2013"/>
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| <ref name="Kolomiyets-2013"/>
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| <ref name="Bethard-2013"/>
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| <ref name="Chambers-2013"/>
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| <ref name="Kolya-2013"/>
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| <ref name="Zavarella-2013"/>
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Time expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| <ref name="Bethard-2015">Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky and Marc Verhagen. [http://www.aclweb.org/anthology/S15-2136 SemEval-2015 Task 6: Clinical TempEval]. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 806-814.</ref>
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| <ref name="Tissot-2015">Hegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski and Marcos Didonet Del Fabro. [http://www.aclweb.org/anthology/S15-2141 UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval]. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 835-839.</ref>
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| <ref name="Tissot-2015"/>
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| <ref name="Velupillai-2015">Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen and Wendy Chapman. [http://www.aclweb.org/anthology/S15-2137 BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge]. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), (Denver, Colorado, June 2015), Association for Computational Linguistics, pp. 815-819.</ref>
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| <ref name="Bethard-2015"/>
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| <ref name="Velupillai-2015"/>
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
Phase 1: text only
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| <ref name="Bethard-2015"/>
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| <ref name="Bethard-2015"/>
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| <ref name="Velupillai-2015"/>
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
|}

Phase 2: manual EVENTs and TIMEX3s
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| <ref name="Bethard-2015"/>
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| <ref name="Bethard-2015"/>
| -
| -
| -
| 0.514
| 0.170
| 0.255
| 0.554
| 0.170
| 0.260
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| <ref name="Velupillai-2015"/>
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==
<references/>

Unsorted
* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

State of the art

2015-06-10T03:29:40Z

Bethard: Renaming page because the TempEvals cover more than just time normalization; they cover events and temporal relations too.

The purpose of this section of the ACL wiki is to be a repository of ''k''-best state-of-the-art results (i.e., methods and software) for various core natural language processing tasks.

As a side effect, this should hopefully evolve into a knowledge base of standard evaluation methods and datasets for various tasks, as well as encourage more effort into reproducibility of results. This will help newcomers to a field appreciate what has been done so far and what the main tasks are, and will help keep active researchers informed on fields other than their specific research. The next time you need a system for PP attachment, or wonder what is the current state of word sense disambiguation, this will be the place to visit.

Please contribute! (This is also a good place for you to display your results!)

As a historical point of reference, you may want to refer to the [http://web.archive.org/web/20100325144600/http://cslu.cse.ogi.edu/HLTsurvey/ Survey of the State of the Art in Human Language Technology] ([http://www.lt-world.org/hlt_survey/master.pdf also available as PDF]), edited by R. Cole, J. Mariani, H. Uszkoreit, G. B. Varile, A. Zaenen, A. Zampolli, V. Zue, 1996.


* [[Anaphora Resolution (State of the art)|Anaphora Resolution]] (stub)
* [[Automatic Text Summarization (State of the art)|Automatic Text Summarization]] (stub)
* [[Chunking (State of the art)|Chunking]] (stub)
* [[Dependency Parsing (State of the art)|Dependency Parsing]] (stub)
* [[Document Classification (State of the art)|Document Classification]] (stub)
* [[Language Identification (State of the art)|Language Identification]] (stub)
* [[Named Entity Recognition (State of the art)|Named Entity Recognition]]
* [[Noun-Modifier Semantic Relations (State of the art)|Noun-Modifier Semantic Relations]]
* [[NP Chunking (State of the art)|NP Chunking]]
* [[Paraphrase Identification (State of the art)|Paraphrase Identification]]
* [[Parsing (State of the art)|Parsing]]
* [[POS Induction (State of the art) |POS Induction]]
* [[POS Tagging (State of the art) |POS Tagging]]
* [[PP Attachment (State of the art)|PP Attachment]] (stub)
* [[Question Answering (State of the art)|Question Answering]]
* [[Semantic Role Labeling (State of the art)|Semantic Role Labeling]] (stub)
* [[Sentiment Analysis (State of the art)|Sentiment Analysis]] (stub)
* [[Similarity (State of the art)|Similarity]] -- [[ESL Synonym Questions (State of the art)|ESL]], [[SAT Analogy Questions (State of the art)|SAT]], [[TOEFL Synonym Questions (State of the art)|TOEFL]], [[RG-65 Test Collection (State of the art)|RG-65 Test Collection]], [[WordSimilarity-353 Test Collection (State of the art)|WordSimilarity-353]], [[SemEval-2012 Task 2 (State of the art)|SemEval-2012 Task 2]]
* [[Speech Recognition (State of the art)|Speech Recognition]] (article request)
* [[Temporal Information Extraction (State of the art)|Temporal Information Extraction]]
* [[Cleaneval (State of the art)| Web Corpus Cleaning]] (stub)
* [[Word Segmentation (State of the art)|Word Segmentation]] (stub)
* [[Word Sense Disambiguation (State of the art)|Word Sense Disambiguation]] (stub)


[[Category:State of the art]]

Temporal Expression Recognition and Normalisation (State of the art)

2015-06-10T03:27:46Z

Bethard: Bethard moved page Temporal Expression Recognition and Normalisation (State of the art) to Temporal Information Extraction (State of the art): TempEvals are more than just TIMEXes; they include events and temporal relations

#REDIRECT [[Temporal Information Extraction (State of the art)]]

Temporal Information Extraction (State of the art)

2015-06-10T03:27:46Z

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Task A: Temporal expression extraction and normalisation====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| <ref name="Stroetgen-2013">Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.</ref>
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| <ref name="Chambers-2013">Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| <ref name="Filannino-2013">Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.</ref>
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| <ref name="Chang-2013">Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| <ref name="Jung-2013">Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.</ref>
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| <ref name="Bethard-2013">Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.</ref>
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| <ref name="Kolya-2013">Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.</ref>
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| <ref name="Kolomiyets-2013">Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.</ref>
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| <ref name="Zavarella-2013">Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.</ref>
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| <ref name="Jung-2013"/>
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| <ref name="Kolomiyets-2013"/>
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| <ref name="Bethard-2013"/>
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| <ref name="Chambers-2013"/>
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| <ref name="Kolya-2013"/>
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| <ref name="Zavarella-2013"/>
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Time expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
Phase 1: text only
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| -
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
|}

Phase 2: manual EVENTs and TIMEX3s
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| -
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.514
| 0.170
| 0.255
| 0.554
| 0.170
| 0.260
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==
<references/>

Unsorted
* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-05-18T19:15:11Z

Bethard: Fixes results as per https://groups.google.com/d/msg/clinical-tempeval/eDNCVkiB6WA/5u8p467S5PsJ

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Task A: Temporal expression extraction and normalisation====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| <ref name="Stroetgen-2013">Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.</ref>
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| <ref name="Chambers-2013">Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| <ref name="Filannino-2013">Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.</ref>
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| <ref name="Chang-2013">Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| <ref name="Jung-2013">Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.</ref>
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| <ref name="Bethard-2013">Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.</ref>
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| <ref name="Kolya-2013">Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.</ref>
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| <ref name="Kolomiyets-2013">Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.</ref>
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| <ref name="Zavarella-2013">Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.</ref>
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| <ref name="Jung-2013"/>
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| <ref name="Kolomiyets-2013"/>
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| <ref name="Bethard-2013"/>
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| <ref name="Chambers-2013"/>
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| <ref name="Kolya-2013"/>
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| <ref name="Zavarella-2013"/>
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Time expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
Phase 1: text only
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| -
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
|}

Phase 2: manual EVENTs and TIMEX3s
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| -
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.514
| 0.170
| 0.255
| 0.554
| 0.170
| 0.260
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==
<references/>

Unsorted
* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-31T14:11:10Z

Bethard: Splits Clinical TempEval relations into two tables so that sorting works.

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Task A: Temporal expression extraction and normalisation====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| <ref name="Stroetgen-2013">Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.</ref>
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| <ref name="Chambers-2013">Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| <ref name="Filannino-2013">Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.</ref>
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| <ref name="Chang-2013">Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| <ref name="Jung-2013">Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.</ref>
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| <ref name="Bethard-2013">Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.</ref>
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| <ref name="Kolya-2013">Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.</ref>
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| <ref name="Kolomiyets-2013">Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.</ref>
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| <ref name="Zavarella-2013">Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.</ref>
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| <ref name="Jung-2013"/>
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| <ref name="Kolomiyets-2013"/>
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| <ref name="Bethard-2013"/>
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| <ref name="Chambers-2013"/>
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| <ref name="Kolya-2013"/>
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| <ref name="Zavarella-2013"/>
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Time expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
Phase 1: text only
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| -
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
|}

Phase 2: manual EVENTs and TIMEX3s
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
| Baseline
| Memorize
| -
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.433
| 0.162
| 0.235
| 0.469
| 0.162
| 0.240
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==
<references/>

Unsorted
* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T19:25:27Z

Bethard: Uses Wikipedia <ref> tags

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Task A: Temporal expression extraction and normalisation====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| <ref name="Stroetgen-2013">Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.</ref>
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| <ref name="Chambers-2013">Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| <ref name="Filannino-2013">Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.</ref>
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| <ref name="Chang-2013">Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.</ref>
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| <ref name="Jung-2013">Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.</ref>
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| <ref name="Bethard-2013">Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.</ref>
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| <ref name="Kolya-2013">Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.</ref>
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| <ref name="Kolomiyets-2013">Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.</ref>
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| <ref name="Zavarella-2013">Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.</ref>
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| <ref name="Jung-2013"/>
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| <ref name="Kolomiyets-2013"/>
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| <ref name="Bethard-2013"/>
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| <ref name="Chambers-2013"/>
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| <ref name="Kolya-2013"/>
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| <ref name="Zavarella-2013"/>
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Time expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
! colspan="14" | Phase 1: text only
|-
| Baseline
| Memorize
| -
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
! colspan="14" | Phase 2: manual EVENTs and TIMEX3s
|-
| Baseline
| Memorize
| -
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.433
| 0.162
| 0.235
| 0.469
| 0.162
| 0.240
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==
<references/>

Unsorted
* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T18:06:49Z

Bethard: Consolidates notes about only showing the best result for each system

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Task A: Temporal expression extraction and normalisation====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| Stro ̈tgen et al., 2013
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| Chambers, 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| Filannino et al., 2013
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| Chang et al., 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| Jung et al., 2013
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| Bethard, 2013
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| Kolya et al., 2013
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| Kolomiyets et al., 2013
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| Zavarella et al., 2013
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| Jung et al., 2013
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| Kolomiyets et al., 2013
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| Bethard, 2013
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| Chambers, 2013
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| Kolya et al., 2013
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| Zavarella et al., 2013
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===
Tables show the best result for each system. Lower scoring runs for the same system are not shown.

====Time expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
! colspan="14" | Phase 1: text only
|-
| Baseline
| Memorize
| -
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
! colspan="14" | Phase 2: manual EVENTs and TIMEX3s
|-
| Baseline
| Memorize
| -
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.433
| 0.162
| 0.235
| 0.469
| 0.162
| 0.240
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==

* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.
* Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.
* Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.
* Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.
* Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.
* Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.
* Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.
* Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.
* Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T18:02:45Z

Bethard: Adds system scores for Clinical TempEval temporal relation task

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===

====Task A: Temporal expression extraction and normalisation====
The table shows the best result for each system. Different runs per system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| Stro ̈tgen et al., 2013
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| Chambers, 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| Filannino et al., 2013
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| Chang et al., 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| Jung et al., 2013
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| Bethard, 2013
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| Kolya et al., 2013
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| Kolomiyets et al., 2013
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| Zavarella et al., 2013
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====

{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| Jung et al., 2013
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| Kolomiyets et al., 2013
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| Bethard, 2013
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| Chambers, 2013
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| Kolya et al., 2013
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| Zavarella et al., 2013
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===

====Time expressions====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | To Document Time
! colspan="6" | Narrative Containers
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! P
! R
! F1
|-
! colspan="14" | Phase 1: text only
|-
| Baseline
| Memorize
| -
| 0.600
| 0.555
| 0.577
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.368
| 0.061
| 0.104
| 0.400
| 0.061
| 0.106
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| 0.712
| 0.693
| 0.702
| 0.080
| 0.142
| 0.102
| 0.094
| 0.179
| 0.123
| -
| -
|-
! colspan="14" | Phase 2: manual EVENTs and TIMEX3s
|-
| Baseline
| Memorize
| -
| -
| -
| 0.608
| -
| -
| -
| -
| -
| -
| -
| -
|-
| Baseline
| TIMEX3 to closest EVENT
| -
| -
| -
| -
| 0.433
| 0.162
| 0.235
| 0.469
| 0.162
| 0.240
| -
| -
|-
| BluLab: run 2
| Supervised machine learning
| -
| -
| -
| 0.791
| 0.109
| 0.210
| 0.143
| 0.140
| 0.254
| 0.181
| -
| -
|-
|}

==References==

* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.
* Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.
* Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.
* Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.
* Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.
* Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.
* Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.
* Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.
* Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T17:51:28Z

Bethard: Adds system scores for Clinical TempEval event expression task

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===

====Task A: Temporal expression extraction and normalisation====
The table shows the best result for each system. Different runs per system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| Stro ̈tgen et al., 2013
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| Chambers, 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| Filannino et al., 2013
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| Chang et al., 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| Jung et al., 2013
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| Bethard, 2013
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| Kolya et al., 2013
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| Kolomiyets et al., 2013
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| Zavarella et al., 2013
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====

{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| Jung et al., 2013
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| Kolomiyets et al., 2013
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| Bethard, 2013
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| Chambers, 2013
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| Kolya et al., 2013
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| Zavarella et al., 2013
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===

====Time expressions====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Modality
! colspan="4" | Degree
! colspan="4" | Polarity
! colspan="4" | Type
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
! P
! R
! F1
! A
|-
| Baseline
| Memorize
| -
| 0.876
| 0.810
| 0.842
| 0.810
| 0.749
| 0.778
| 0.924
| 0.871
| 0.806
| 0.838
| 0.995
| 0.800
| 0.740
| 0.769
| 0.913
| 0.846
| 0.783
| 0.813
| 0.966
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.887
| 0.864
| 0.875
| 0.834
| 0.813
| 0.824
| 0.942
| 0.882
| 0.859
| 0.870
| 0.994
| 0.868
| 0.846
| 0.857
| 0.979
| 0.834
| 0.812
| 0.823
| 0.941
| -
| -
|-
|}

====Temporal relations====

==References==

* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.
* Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.
* Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.
* Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.
* Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.
* Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.
* Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.
* Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.
* Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T17:40:14Z

Bethard: Makes tables sortable

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===

====Task A: Temporal expression extraction and normalisation====
The table shows the best result for each system. Different runs per system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| Stro ̈tgen et al., 2013
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| Chambers, 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| Filannino et al., 2013
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| Chang et al., 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| Jung et al., 2013
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| Bethard, 2013
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| Kolya et al., 2013
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| Kolomiyets et al., 2013
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| Zavarella et al., 2013
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====

{| width="100%" class="wikitable sortable"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| Jung et al., 2013
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| Kolomiyets et al., 2013
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| Bethard, 2013
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| Chambers, 2013
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| Kolya et al., 2013
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| Zavarella et al., 2013
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===

====Time expressions====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====

====Temporal relations====

==References==

* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.
* Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.
* Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.
* Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.
* Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.
* Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.
* Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.
* Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.
* Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T17:35:55Z

Bethard: Adds system scores for Clinical TempEval time expression task

== TempEval 2007 ==
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]

== TempEval 2010 ==
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]

== TempEval 2013 ==
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]

=== Performance measures ===

=== Results ===

====Task A: Temporal expression extraction and normalisation====
The table shows the best result for each system. Different runs per system are not shown.
{| border="1" cellpadding="5" cellspacing="1" width="100%"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="6" | Identification
! colspan="2" | Normalisation
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Lenient matching
! colspan="2" | Accuracy
|-
! Pre.
! Rec.
! F1
! Pre.
! Rec.
! F1
! Type
! Value
|-
| HeidelTime (t)
| rule-based
| Stro ̈tgen et al., 2013
| 83.85
| 78.99
| 81.34
| 93.08
| 87.68
| 90.30
| 90.91
| '''85.95'''
| '''77.61'''
| [http://dbs.ifi.uni-heidelberg.de/index.php?id=129 Download]
| [http://www.gnu.org/licenses/gpl.html GNU GPL v3]
|-
| NavyTime (1,2)
| rule-based
| Chambers, 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 78.58
| 70.97
| -
| -
|-
| ManTIME (4)
| CRF, probabilistic post-processing pipeline, rule-based normaliser
| Filannino et al., 2013
| 78.86
| 70.29
| 74.33
| 95.12
| 84.78
| 89.66
| 86.31
| 76.92
| 68.97
| [http://www.cs.man.ac.uk/~filannim/projects/tempeval-3/ Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| SUTime
| deterministic rule-based
| Chang et al., 2013
| 78.72
| '''80.43'''
| 79.57
| 89.36
| '''91.30'''
| '''90.32'''
| 88.90
| 74.60
| 67.38
| [http://nlp.stanford.edu/software/sutime.shtml Demo & Download]
| [http://www.gnu.org/licenses/gpl-2.0.html GNU GPL v2]
|-
| ATT (2)
| MaxEnt, third party normalisers
| Jung et al., 2013
| '''90.57'''
| 69.57
| 78.69
| '''98.11'''
| 75.36
| 85.25
| 91.34
| 76.91
| 65.57
| -
| -
|-
| ClearTK (1,2)
| SVM, Logistic Regression, third party normaliser
| Bethard, 2013
| 85.94
| 79.71
| '''82.71'''
| 93.75
| 86.96
| 90.23
| '''93.33'''
| 71.66
| 64.66
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| JU-CSE
| CRF, rule-based normaliser
| Kolya et al., 2013
| 81.51
| 70.29
| 75.49
| 93.28
| 80.43
| 86.38
| 87.39
| 73.87
| 63.81
| -
| -
|-
| KUL (2)
| Logistic regression, post-processing, rule-based normaliser
| Kolomiyets et al., 2013
| 76.99
| 63.04
| 69.32
| 92.92
| 76.09
| 83.67
| 88.56
| 75.24
| 62.95
| -
| -
|-
| FSS-TimEx
| rule-based
| Zavarella et al., 2013
| 52.03
| 46.38
| 49.04
| 90.24
| 80.43
| 85.06
| 81.08
| 68.47
| 58.24
| -
| -
|-
|}

====Task B: Event extraction and classification====

{| border="1" cellpadding="5" cellspacing="1" width="100%"
|-
! rowspan="3" | System name (best run)
! rowspan="3" | Short description
! rowspan="3" | Main publication
! colspan="3" | Identification
! colspan="3" | Attributes
! rowspan="3" | Overall score
! rowspan="3" | Software
! rowspan="3" | License
|-
! colspan="3" | Strict matching
! colspan="3" | Accuracy
|-
! Pre.
! Rec.
! F1
! Class
! Tense
! Aspect
|-
| ATT (1)
|
| Jung et al., 2013
| 81.44
| 80.67
| '''81.05'''
| 88.69
| 73.37
| 90.68
| '''71.88'''
|
|
|-
| KUL (2)
|
| Kolomiyets et al., 2013
| 80.69
| 77.99
| 79.32
| 88.46
| -
| -
| 70.17
|
|
|-
| ClearTK (4)
|
| Bethard, 2013
| 81.40
| 76.38
| 78.81
| 86.12
| 78.20
| 90.86
| 67.87
| [https://code.google.com/p/cleartk/ Download]
| [http://opensource.org/licenses/BSD-3-Clause BSD-3 Clause]
|-
| NavyTime (1)
|
| Chambers, 2013
| 80.73
| 79.87
| 80.30
| 84.03
| 75.79
| 91.26
| 67.48
|
|
|-
| Temp: (ESAfeature)
|
| X, 2013
| 78.33
| 61.61
| 68.97
| 79.09
| -
| -
| 54.55
|
|
|-
| JU_CSE
|
| Kolya et al., 2013
| 80.85
| 76.51
| 78.62
| 67.02
| 74.56
| 91.76
| 52.69
|
|
|-
| FSS-TimeEx
|
| Zavarella et al., 2013
| 63.13
| 67.11
| 65.06
| 66.00
| -
| -
| 42.94
|
|
|-
|}

====Task C: Annotating relations given gold entities====

====Task C relation only: Annotating relations given gold entities and related pairs====

====Task ABC: Temporal awareness evaluation====

== Clinical TempEval 2015 ==
* '''Clinical TempEval 2015''', ''Clinical TempEval'', 2015: [http://alt.qcri.org/semeval2015/task6/ web page]

=== Performance measures ===

=== Results ===

====Time expressions====
The table shows the best result for each system. Lower scoring runs for the same system are not shown.
{| border="1" cellpadding="5" cellspacing="1" width="100%" class="wikitable sortable"
|-
! rowspan="2" | System name (best run)
! rowspan="2" | Short description
! rowspan="2" | Main publication
! colspan="3" | Span
! colspan="4" | Class
! rowspan="2" | Software
! rowspan="2" | License
|-
! P
! R
! F1
! P
! R
! F1
! A
|-
| Baseline: memorize
| -
| -
| 0.743
| 0.372
| 0.496
| 0.723
| 0.362
| 0.483
| 0.974
| -
| -
|-
| KPSCMI: run 1
| Rule-based
| -
| 0.272
| 0.782
| 0.404
| 0.223
| 0.642
| 0.331
| 0.819
| -
| -
|-
| KPSCMI: run 3
| Supervised machine learning
| -
| 0.693
| 0.706
| 0.699
| 0.657
| 0.669
| 0.663
| 0.948
| -
| -
|-
| UFPRSheffield-SVM: run 2
| Supervised machine learning
| -
| 0.741
| 0.655
| 0.695
| 0.723
| 0.640
| 0.679
| 0.977
| -
| -
|-
| UFPRSheffield-Hynx: run 5
| Rule-based
| -
| 0.411
| 0.795
| 0.542
| 0.391
| 0.756
| 0.516
| 0.952
| -
| -
|-
| BluLab: run 1-3
| Supervised machine learning
| -
| 0.797
| 0.664
| 0.725
| 0.778
| 0.652
| 0.709
| 0.978
| -
| -
|-
|}

====Event expressions====

====Temporal relations====

==References==

* UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. [http://www.aclweb.org/anthology/S/S13/S13-2001.pdf Semeval-2013 task 1: Tempeval-3: Evaluating time expressions, events, and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 1–9.
* Bethard, S. [http://www.aclweb.org/anthology/S/S13/S13-2002.pdf ClearTK-TimeML: A minimalist approach to tempeval 2013]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), vol. 2, Association for Computational Linguistics, Association for Computational Linguistics, pp. 10–14.
* Stro ̈tgen, J., Zell, J., and Gertz, M. [http://www.aclweb.org/anthology/S/S13/S13-2003.pdf Heideltime: Tuning english and developing spanish resources for tempeval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 15–19.
* Jung, H., and Stent, A. [http://www.aclweb.org/anthology/S/S13/S13-2004.pdf ATT1: Temporal annotation using big windows and rich syntactic and semantic features]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 20–24.
* Filannino, M., Brown, G., and Nenadic, G. [http://www.aclweb.org/anthology/S/S13/S13-2009.pdf ManTIME: Temporal expression identification and normalization in the Tempeval-3 challenge]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evalu- ation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 53–57.
* Zavarella, V., and Tanev, H. [http://www.aclweb.org/anthology/S/S13/S13-2010.pdf FSS-TimEx for tempeval-3: Extracting temporal information from text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 58–63.
* Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. [http://www.aclweb.org/anthology/S/S13/S13-2011.pdf JU_CSE: A CRF based approach to annotation of temporal expression, event and temporal relations]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 64–72.
* Chambers, N. [http://www.aclweb.org/anthology/S/S13/S13-2012.pdf Navytime: Event and time ordering from raw text]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 73–77.
* Chang, A., and Manning, C. D. [http://www.aclweb.org/anthology/S/S13/S13-2013.pdf SUTime: Evaluation in TempEval-3]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 78–82.
* Kolomiyets, O., and Moens, M.-F. [http://www.aclweb.org/anthology/S/S13/S13-2014.pdf KUL: Data-driven approach to temporal parsing of newswire articles]. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceed- ings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 83–87.
* Laokulrat, N., Miwa, M., Tsuruoka, Y., and Chikayama, T. [http://www.aclweb.org/anthology/S/S13/S13-2015.pdf UTTime: Temporal relation classification using deep syntactic features]. In Second Joint Conference on Lexical and Computational Se- mantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Atlanta, Georgia, USA, June 2013), Association for Computational Linguistics, pp. 88– 92.

== See also ==
* [[State of the art]]

== External links ==
* [http://timexportal.info TimexPortal]

[[Category:State of the art]]

Temporal Information Extraction (State of the art)

2015-03-30T17:14:31Z

Bethard: Reorganizes headings to allow addition of Clinical TempEval results

Conference acceptance rates

2007-04-23T23:47:34Z

Bethard: /* EMNLP */

==[[ACL]] - Main session==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2003</td>
<td>360</td>
<td>71</td>
<td>20%</td>
</tr>
<tr>
<td>2004</td>
<td>348</td>
<td>88</td>
<td>25%</td>
</tr>
<tr>
<td>2005</td>
<td>423</td>
<td>77</td>
<td>18%</td>
</tr>
</table>

==ACL - Student session==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1992</td>
<td>48</td>
<td>20</td>
<td>42%</td>
</tr>
<tr>
<td>1993</td>
<td>30</td>
<td>11</td>
<td>37%</td>
</tr>
<tr>
<td>1994</td>
<td>41</td>
<td>10</td>
<td>24%</td>
</tr>
<tr>
<td>1995</td>
<td>48</td>
<td>19</td>
<td>24%</td>
</tr>
<tr>
<td>1996</td>
<td>32</td>
<td>14</td>
<td>44%</td>
</tr>
<tr>
<td>1997</td>
<td>42</td>
<td>10</td>
<td>24%</td>
</tr>
<tr>
<td>1998</td>
<td>46</td>
<td>12</td>
<td>26%</td>
</tr>
<tr>
<td>1999</td>
<td>30</td>
<td>10</td>
<td>33%</td>
</tr>
<tr>
<td>2000</td>
<td>36</td>
<td>10</td>
<td>28%</td>
</tr>
<tr>
<td>2001</td>
<td>?</td>
<td>?</td>
<td>?</td>
</tr>
<tr>
<td>2005</td>
<td>70</td>
<td>26</td>
<td>37%</td>
</tr>
<tr>
<td>2006</td>
<td>40</td>
<td>15</td>
<td>38%</td>
</tr>

</table>

==ACL - Posters==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
</table>

==[[HLT-NAACL]] - Main session==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td>110</td>
<td>31</td>
<td>28.2%</td>
</tr>
<tr>
<td>2003</td>
<td>162</td>
<td>37</td>
<td>22.8%</td>
</tr>
<tr>
<td>2006</td>
<td>257</td>
<td>62</td>
<td>24.1%</td>
</tr>
<tr>
<td>2007</td>
<td>298</td>
<td>72</td>
<td>24.2%</td>
</tr>
</table>

==HLT-NAACL - Student session==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
</table>

==[[EMNLP]]==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2004</td>
<td>247</td>
<td>58</td>
<td>23.5%</td>
</tr>
<tr>
<td>2005</td>
<td></td>
<td>98</td>
<td>25%</td>
</tr>
<tr>
<td>2006</td>
<td>234</td>
<td>73</td>
<td>31%</td>
</tr>
</table>

==[[EACL]]==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2006</td>
<td> 264 </td>
<td> 52 </td>
<td>19.7%</td>
</tr>
</table>

==EACL - Student session==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2003</td>
<td></td>
<td></td>
<td></td>
</tr>

<tr>
<td>2006</td>
<td>33</td>
<td>9</td>
<td>27.3%</td>
</tr>
</table>

==[[IJCNLP]]==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>

</table>

==[[RANLP]]==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
</table>

==[[CICLING]]==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2003</td>
<td></td>
<td></td>
<td>47%</td>
</tr>
</table>

==[[CONLL]]==

<table cellspacing="1" cellpadding="1" border="0" width="100%">
<tr>
<td>Year</td>
<td>Submitted</td>
<td>Accepted</td>
<td>Acceptance Rate</td>
</tr>
<tr>
<td>1999</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2000</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2001</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>2005</td>
<td>70</td>
<td>19</td>
<td>27%</td>
</tr>
</table>

[[Category:Conferences]]