Difference between revisions of "Temporal Information Extraction (State of the art)"

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(Fixes results as per https://groups.google.com/d/msg/clinical-tempeval/eDNCVkiB6WA/5u8p467S5PsJ)
(24 intermediate revisions by 2 users not shown)
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−
==Data sets==
+
== 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==
+
=== Performance measures ===
  
−
==Results==
+
=== Results ===
−
The following results refers to the TempEval-3 challenge, the last evaluation exercise.
+
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===
+
====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"
−
{| border="1" cellpadding="5" cellspacing="1" width="100%"
 
 
|-
 
|-
−
! rowspan="3" | System name
+
! rowspan="3" | System name (best run)
 
! rowspan="3" | Short description
 
! rowspan="3" | Short description
 
! rowspan="3" | Main publication
 
! rowspan="3" | Main publication
Line 32: Line 38:
 
! Value
 
! Value
 
|-
 
|-
−
| HeidelTime
+
| HeidelTime (t)
−
|  
+
| rule-based
−
| Stro ̈tgen et al., 2013
+
| <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
+
| NavyTime (1,2)
−
|  
+
| rule-based
−
| Chambers et al., 2013
+
| <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
+
| ManTIME (4)
−
|  
+
| CRF, probabilistic post-processing pipeline, rule-based normaliser
−
| Filannino et al., 2013
+
| <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
 
| SUTime
−
|  
+
| deterministic rule-based
−
| Chang et al., 2013
+
| <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
+
| ATT (2)
−
|  
+
| MaxEnt, third party normalisers
−
| Jung et al., 2013
+
| <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
+
| ClearTK (1,2)
−
|  
+
| SVM, Logistic Regression, third party normaliser
−
| Bethard, 2013
+
| <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
 
| 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
 
|  
 
|  
 
|  
 
|  
 
|-
 
|-
−
| KUL
+
| JU_CSE
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
 
|  
 
|  
 +
| <ref name="Kolya-2013"/>
 +
| 80.85
 +
| 76.51
 +
| 78.62
 +
| 67.02
 +
| 74.56
 +
| 91.76
 +
| 52.69
 
|  
 
|  
 
|  
 
|  
 
|-
 
|-
−
| FSS-TimEx
+
| FSS-TimeEx
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
−
|
 
 
|  
 
|  
 +
| <ref name="Zavarella-2013"/>
 +
| 63.13
 +
| 67.11
 +
| 65.06
 +
| 66.00
 +
| -
 +
| -
 +
| 42.94
 
|  
 
|  
 
|  
 
|  
Line 169: Line 290:
 
|}
 
|}
  
−
===Task B: Event extraction and classification===
+
====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
 +
| -
 +
| -
 +
|-
 +
|}
  
−
===Task C: Annotating relations given gold entities===
+
====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
 +
| -
 +
| -
 +
|-
 +
|}
  
−
===Task C relation only: Annotating relations given gold entities and related pairs===
+
====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
 +
| -
 +
| -
 +
|-
 +
|}
  
−
==Challenges==
+
Phase 2: manual EVENTs and TIMEX3s
−
* '''TempEval''', ''Temporal Relation Identification'', 2007: [http://www.timeml.org/tempeval/ web page]
+
{| width="100%" class="wikitable sortable"
−
* '''TempEval-2''', ''Evaluating Events, Time Expressions, and Temporal Relations'', 2010: [http://www.timeml.org/tempeval2/ web page]
+
|-
−
* '''TempEval-3''', ''Evaluating Time Expressions, Events, and Temporal Relations'', 2013: [http://www.cs.york.ac.uk/semeval-2013/task1/ web page]
+
! 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==
 +
<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.
 
* 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.
 
* 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.
  

Revision as of 13:15, 18 May 2015

TempEval 2007

  • TempEval, Temporal Relation Identification, 2007: web page

TempEval 2010

  • TempEval-2, Evaluating Events, Time Expressions, and Temporal Relations, 2010: web page

TempEval 2013

  • TempEval-3, Evaluating Time Expressions, Events, and Temporal Relations, 2013: 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

System name (best run) Short description Main publication Identification Normalisation Overall score Software License
Strict matching Lenient matching Accuracy
Pre. Rec. F1 Pre. Rec. F1 Type Value
HeidelTime (t) rule-based [1] 83.85 78.99 81.34 93.08 87.68 90.30 90.91 85.95 77.61 Download GNU GPL v3
NavyTime (1,2) rule-based [2] 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 [3] 78.86 70.29 74.33 95.12 84.78 89.66 86.31 76.92 68.97 Demo & Download GNU GPL v2
SUTime deterministic rule-based [4] 78.72 80.43 79.57 89.36 91.30 90.32 88.90 74.60 67.38 Demo & Download GNU GPL v2
ATT (2) MaxEnt, third party normalisers [5] 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 [6] 85.94 79.71 82.71 93.75 86.96 90.23 93.33 71.66 64.66 Download BSD-3 Clause
JU-CSE CRF, rule-based normaliser [7] 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 [8] 76.99 63.04 69.32 92.92 76.09 83.67 88.56 75.24 62.95 - -
FSS-TimEx rule-based [9] 52.03 46.38 49.04 90.24 80.43 85.06 81.08 68.47 58.24 - -

Task B: Event extraction and classification

System name (best run) Short description Main publication Identification Attributes Overall score Software License
Strict matching Accuracy
Pre. Rec. F1 Class Tense Aspect
ATT (1) [5] 81.44 80.67 81.05 88.69 73.37 90.68 71.88
KUL (2) [8] 80.69 77.99 79.32 88.46 - - 70.17
ClearTK (4) [6] 81.40 76.38 78.81 86.12 78.20 90.86 67.87 Download BSD-3 Clause
NavyTime (1) [2] 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 [7] 80.85 76.51 78.62 67.02 74.56 91.76 52.69
FSS-TimeEx [9] 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: 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

System name (best run) Short description Main publication Span Class Software 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

System name (best run) Short description Main publication Span Modality Degree Polarity Type Software 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

System name (best run) Short description Main publication To Document Time Narrative Containers Software 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

System name (best run) Short description Main publication To Document Time Narrative Containers Software 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

  1. ↑ Stro ̈tgen, J., Zell, J., and Gertz, M. 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.
  2. ↑ 2.0 2.1 Chambers, N. 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.
  3. ↑ Filannino, M., Brown, G., and Nenadic, G. 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.
  4. ↑ Chang, A., and Manning, C. D. 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.
  5. ↑ 5.0 5.1 Jung, H., and Stent, A. 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.
  6. ↑ 6.0 6.1 Bethard, S. 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.
  7. ↑ 7.0 7.1 Kolya, A. K., Kundu, A., Gupta, R., Ekbal, A., and Bandyopadhyay, S. 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.
  8. ↑ 8.0 8.1 Kolomiyets, O., and Moens, M.-F. 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.
  9. ↑ 9.0 9.1 Zavarella, V., and Tanev, H. 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.

Unsorted

  • UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M., and Pustejovsky, J. 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. 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

External links

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