Difference between revisions of "Textual Entailment Portal"

From ACL Wiki
Jump to navigation Jump to search
(Moved the references to a seperate page)
Line 15: Line 15:
 
The entailment need not be pure logical - it has a more relaxed definition: "t entails h (t ⇒ h) if, typically, a human reading t would infer that h is most likely true."<ref>I. Dagan</ref>
 
The entailment need not be pure logical - it has a more relaxed definition: "t entails h (t ⇒ h) if, typically, a human reading t would infer that h is most likely true."<ref>I. Dagan</ref>
  
This page serves as a community portal for everything related to Textual Entailment.  
+
Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.
  
== Textual Entailment Resource Pool ==
+
This page serves as a community portal for everything related to Textual Entailment:
[[Textual Entailment Resource Pool]]
+
* [[Textual Entailment Resource Pool]] - Entailment engines, demos, knowledge resources, etc.
 
+
* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]
== PASCAL Challenges ==
+
* [[Textual Entailment References]] - workshops, tutorials and papers.
 
 
[[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]] has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.
 
 
 
== References on Textual Entailment ==
 
''You are welcome to update this list with new papers on textual entailment (please keep the new references in the same format, and  maintain the alphabetical order).''
 
 
 
=== Workshops and Tutorials ===
 
 
 
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]
 
 
 
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]
 
 
 
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]
 
 
 
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]
 
 
 
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]
 
 
 
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]
 
 
 
=== Papers in recent conferences and other workshops ===
 
 
 
L. Bentivogli, I. Dagan, H. Dang, D. Giampiccolo, M. Lo Leggio, and B. Magnini . 2009. Considering Discourse References in Textual Entailment Annotation. 5th International Conference on Generative Approaches to the Lexicon (GL 2009). [http://hlt.fbk.eu/sites/hlt.fbk.eu/files/GL2009_Bentivogli-et-al.pdf pdf]
 
 
 
J. Bos, K. Markert. 2005. Recognising Textual Entailment with Logical Inference. Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP 2005), pp. 628–635. [http://www.meaningfactory.com/bos/pubs/BosMarkert2005EMNLP.pdf pdf]
 
 
 
R. Braz, R. Girju, V. Punyakanok, D. Roth, and M. Sammons. 2005. An Inference Model for Semantic Entailment in Natural Language. Twentieth National Conference on Artificial Intelligence (AAAI-05)
 
 
 
R. Braz, R. Girju, V. Punyakanok, D. Roth, and M. Sammons. 2005. Knowledge Representation for Semantic Entailment and Question-Answering. IJCAI-05 Workshop on Knowledge and Reasoning for Answering Questions.
 
 
 
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications.
 
RANLP-05.
 
 
 
I. Dagan and O. Glickman. 2004. Probabilistic textual entailment: Generic applied modeling of language variability. In PASCAL Workshop on Learning Methods for Text Understanding and Mining, Grenoble.
 
 
 
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006
 
 
 
R. Delmonte, 2005. VENSES - a Linguistically-Based System for Semantic Evaluation, PLN, Procesamiento del Lenguaje Natural, Revista n° 35, ISSN:1135-5948, pp. 449-450.
 
 
 
R. Delmonte, 2005. Simulare la comprensione del linguaggio con VENSES. presented at Workshop "Scienze Cognitive Applicate", Facolt? di Psicologia dell'Universit? Roma "La Sapienza", 12/13-12-2005.
 
 
 
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.
 
 
 
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).
 
 
 
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA.
 
 
 
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05)
 
 
 
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).
 
 
 
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.
 
 
 
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006
 
 
 
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.
 
 
 
V. Jijkoun and M. de Rijke. 2006. Recognizing Textual Entailment: Is Lexical Similarity Enough?,  In: I. Dagan, F. Dalche, J. Quinonero Candela, B. Magnini, editors, Evaluating Predictive Uncertainty, Textual Entailment and Object Recognition Systems, LNAI 3944, pages 449-460, Springer Verlag.
 
 
 
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.
 
 
 
B. MacCartney, T. Grenager, M. de Marneffe, D. Cer and C. D. Manning. 2006. Learning to Recognize Features of Valid Textual Entailments. HLT-NAACL 2006.
 
 
 
M. Makatchev, P. W. Jordan, K. Vanlehn. 2004. Abductive Theorem Proving for Analyzing Student Explanations to Guide Feedback in Intelligent Tutoring Systems. Journal of Automated Reasoning, 32(3)
 
 
 
Y. Mehdad, B. Magnini. 2009. A Word Overlap Baseline for the Recognizing Textual Entailment Task. Available at http://hlt.fbk.eu/sites/hlt.fbk.eu/files/baseline.pdf
 
 
 
Shachar Mirkin, Ido Dagan, Maayan Geffet. 2006. Integrating Pattern-based and Distributional Similarity Methods for Lexical Entailment Acquisition. COLING-ACL 2006 [http://aclweb.org/anthology-new/P/P06/P06-2075.pdf pdf]
 
 
 
Shachar Mirkin, Ido Dagan, Eyal Shnarch. 2009. Evaluating the Inferential Utility of Lexical-Semantic Resources. EACL-09. [http://www.cs.biu.ac.il/~mirkins/publications/Inferential-Utility_Mirkin-DS_EACL09.pdf pdf]
 
 
 
Shachar Mirkin, Lucia Specia, Nicola Cancedda, Ido Dagan, Marc Dymetman and Idan Szpektor. 2009. Source-Language Entailment Modeling for Translating Unknown Terms. ACL-09. [http://www.cs.biu.ac.il/~mirkins/publications/TE4MT_ACL09_Mirkin-Specia-etal.pdf pdf]
 
 
 
Shachar Mirkin, Ido Dagan and Sebastian Padó. 2010. Assessing the Role of Discourse References in Entailment Inference. ACL-10 [http://aclweb.org/anthology-new/P/P10/P10-1123.pdf pdf]
 
 
 
Shachar Mirkin, Jonathan Berant, Ido Dagan and Eyal Shnarch. 2010. Recognising Entailment within Discourse. COLING-10. [http://www.cs.biu.ac.il/~mirkins/publications/Mirkin-etal_COLING-2010.pdf pdf]
 
 
 
C. Monz and M. de Rijke. 2001. Light-Weight Entailment Checking for Computational Semantics,  In: P. Blackburn and M. Kohlhase, editors, International workshop on Inference in Computational Semantics (ICoS-3).
 
 
 
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).
 
 
 
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006
 
 
 
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006
 
 
 
A. Peñas, A. Rodrigo, F. Verdejo. 2006. SPARTE, a Test Suite for Recognising Textual Entailment in Spanish. Computational Linguistics and Intelligent Text Processing, CICLing 2006. LNCS 3878. 275-286
 
 
 
R. Raina, A. Y. Ng, and C. Manning. 2005. Robust textual inference via learning and abductive reasoning. Twentieth National Conference on Artificial Intelligence (AAAI-05)
 
 
 
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006.
 
 
 
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.
 
 
 
Mark Sammons, Vinod Vydiswaran, and Dan Roth. 2010. Ask not what Textual Entailment can do for you.... ACL-10  [http://l2r.cs.uiuc.edu/~danr/Papers/SammonsVyRo10.pdf pdf]
 
 
 
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.
 
 
 
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.
 
 
 
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster).
 
 
 
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.
 
 
 
Rui Wang and Yajing Zhang. 2008. Recognizing Textual Entailment with Temporal Expressions in Natural Language Texts. In Proceedings of the IEEE International Workshop on Semantic Computing and Applications (IWSCA-2008).
 
 
 
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.
 
 
 
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.
 
 
 
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006
 
 
 
=== Journal papers ===
 
 
 
I. Androutsopoulos and  P. Malakasiotis. 2010. A Survey of Paraphrasing and Textual Entailment Methods. Journal of Artificial Intelligence Research, vol. 38, pp. 135-187. [http://www.jair.org/papers/paper2985.html]
 
  
 
[[Category:Textual Entailment Portal]]
 
[[Category:Textual Entailment Portal]]

Revision as of 06:09, 22 December 2010

Textual Entailment (TE) is the task of judging whether the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text and hypothesis, respectively.

An example of a positive TE (text entails hypothesis) is:

  • text: If you help the needy, God will reward you.
  • hypothesis: Giving money to a poor man has good consequences.

An example of a negative TE (text contradicts hypothesis) is:

  • text: If you help the needy, God will reward you.
  • hypothesis: Giving money to a poor man has no consequences.

An example of a non-TE (text does not entail nor contradict) is:

  • text: If you help the needy, God will reward you.
  • hypothesis: Giving money to a poor man will make you better person.

The entailment need not be pure logical - it has a more relaxed definition: "t entails h (t ⇒ h) if, typically, a human reading t would infer that h is most likely true."[1]

Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.

This page serves as a community portal for everything related to Textual Entailment:

  1. I. Dagan