Difference between revisions of "Textual Entailment Portal"

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This page serves as a community portal for everything related to Textual Entailment.  
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'''Textual Entailment''' (TE) is a directional relation between text fragments. The relation holds whenever 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.  
  
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== Textual Entailment Resource Pool ==
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An example of a positive TE (text entails hypothesis) is:
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[[Textual Entailment Resource Pool]]
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* text: ''If you help the needy, God will reward you''.
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* hypothesis: ''Giving money to a poor man has good consequences''.
  
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== PASCAL Challenges ==
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An example of a negative TE (text contradicts hypothesis) is:
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* text: ''If you help the needy, God will reward you''.
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* hypothesis: ''Giving money to a poor man has no consequences''.
  
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[[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.
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An example of a non-TE (text does not entail nor contradict) is:
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* text: ''If you help the needy, God will reward you''.
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* hypothesis: ''Giving money to a poor man will make you better person''.
  
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== References on Textual Entailment ==
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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>[http://u.cs.biu.ac.il/~dagan/publications/RTEChallenge.pdf Ido Dagan, Oren Glickman and Bernardo Magnini. The PASCAL Recognising Textual Entailment Challenge, p. 2] ''in:'' Quiñonero-Candela, J.; Dagan, I.; Magnini, B.; d'Alché-Buc, F. (Eds.) ''Machine Learning Challenges. Lecture Notes in Computer Science'' , Vol. 3944, pp. 177-190, Springer, 2006.</ref>
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''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).''
 
  
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=== Workshops ===
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Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.
  
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[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]
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[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]
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This page serves as a community portal for everything related to Textual Entailment:
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* [[Textual Entailment Resource Pool]] - Complete RTE Systems, RTE data sets, Knowledge Resources, Tools (Parsers, Role Labelling, Entity Recognition Tools, Similarity / Relatedness Tools, Corpus Readers, Related Libraries), Links.
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[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]
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* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]
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* [[Textual Entailment References]] - Workshops, Tutorials and Papers.
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[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]
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==Notes==
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<references/>
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[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]
 
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=== Papers in recent conferences and other workshops ===
 
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J. Bos & K. Markert. 2005. Recognising Textual Entailment with Logical Inference. Proceedings of EMNLP 2005.
 
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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)
 
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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.
 
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C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications.
 
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RANLP-05.
 
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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.
 
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I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006
 
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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.
 
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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.
 
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M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).
 
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M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA.
 
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O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05)
 
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O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).
 
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A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.
 
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S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006
 
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J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.
 
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M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.
 
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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.
 
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S. Mirkin, I. Dagan, M. Geffet. 2006. Integrating Pattern-based and Distributional Similarity Methods for Lexical Entailment Acquisition. COLING-ACL 2006 (poster)  
 
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R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).
 
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M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006
 
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V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006
 
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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
 
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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)
 
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L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006.
 
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V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.
 
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R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.
 
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M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.
 
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M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster).
 
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F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006
 
  
 
[[Category:Textual Entailment Portal]]
 
[[Category:Textual Entailment Portal]]

Latest revision as of 02:42, 7 August 2013

Textual Entailment (TE) is a directional relation between text fragments. The relation holds whenever 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:

Notes

  1. ↑ Ido Dagan, Oren Glickman and Bernardo Magnini. The PASCAL Recognising Textual Entailment Challenge, p. 2 in: Quiñonero-Candela, J.; Dagan, I.; Magnini, B.; d'Alché-Buc, F. (Eds.) Machine Learning Challenges. Lecture Notes in Computer Science , Vol. 3944, pp. 177-190, Springer, 2006.
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