https://aclweb.org/aclwiki/api.php?action=feedcontributions&user=Erel+Segal&feedformat=atomACL Wiki - User contributions [en]2024-03-29T10:48:53ZUser contributionsMediaWiki 1.35.2https://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Portal&diff=8588Textual Entailment Portal2010-12-30T10:51:45Z<p>Erel Segal: </p>
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<div>'''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. <br />
<br />
An example of a positive TE (text entails hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has good consequences''.<br />
<br />
An example of a negative TE (text contradicts hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has no consequences''.<br />
<br />
An example of a non-TE (text does not entail nor contradict) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man will make you better person''.<br />
<br />
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><br />
<br />
Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.<br />
<br />
This page serves as a community portal for everything related to Textual Entailment:<br />
* [[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.<br />
* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]<br />
* [[Textual Entailment References]] - Workshops, Tutorials and Papers.<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Recognizing_Textual_Entailment&diff=8578Recognizing Textual Entailment2010-12-22T12:14:29Z<p>Erel Segal: </p>
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<div>[[Textual Entailment]] &gt; '''Challenges''':<br />
----<br />
<br />
'''[[Textual Entailment]]''' Recognition has been proposed recently as a generic task that captures major semantic inference needs across many [[Natural Language Processing|NLP]] applications, such as [[Question Answering]], [[Information Retrieval]], [[Information Extraction]], and [[Text Summarization]]. This task requires to recognize, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text.<br />
<br />
== History ==<br />
* [http://www.pascal-network.org/Challenges/RTE/ RTE-1] Pascal<br />
* [http://www.pascal-network.org/Challenges/RTE2/ RTE-2] Pascal<br />
* [http://www.pascal-network.org/Challenges/RTE3/ RTE-3] Pascal<br />
* [http://www.nist.gov/tac/tracks/2008/rte/ RTE-4] TAC 2008<br />
* [http://www.nist.gov/tac/2009/RTE/ RTE-5] TAC 2009<br />
* [http://www.nist.gov/tac/2010/RTE/index.html RTE-6] TAC 2010<br />
<br />
== [[Textual Entailment Resource Pool|Resource Pool]] ==<br />
<br />
In an effort to determine the relative impact of the many resources used withing the RTE challenge, RTE-3 initiated a new activity for building a '''[[Textual Entailment Resource Pool]]'''. RTE participants and any other member of the NLP community are encouraged to contribute to the RTE Resource Pool.<br />
<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Resource_Pool&diff=8577Textual Entailment Resource Pool2010-12-22T12:13:28Z<p>Erel Segal: </p>
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<div>[[Textual Entailment]] &gt; '''Resources''':<br />
----<br />
<br />
[[Textual Entailment|Textual entailment]] systems rely on many different types of [[Natural Language Processing|NLP]] resources, including term banks, paraphrase lists, parsers, named-entity recognizers, etc. With so many resources being continuously released and improved, it can be difficult to know which particular resource to use when developing a system.<br />
<br />
In response, the [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]] shared task community initiated a new activity for building this ''Textual Entailment Resource Pool''. RTE participants and any other member of the NLP community are encouraged to contribute to the pool.<br />
<br />
In an effort to determine the relative impact of the resources, RTE participants are strongly encouraged to report, whenever possible, the contribution to the overall performance of each utilized resource. Formal qualitative and quantitative results should be included in a separate section of the system report as well as posted on the talk pages of this Textual Entailment Resource Pool.<br />
<br />
'''Adding''' a new resource is very easy. See how to '''use existing templates''' to do this in [[Help:Using Templates]].<br />
<br />
== Complete RTE Systems ==<br />
<br />
* [http://project.cgm.unive.it/html/venses.html VENSES] (from Ca' Foscari University of Venice, Italy)<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc/wiki/nutcracker Nutcracker] (available for download)<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/kindleDemo.php Entailment Demo] (from the University of Illinois at Urbana-Champaign) - INACTIVE (as of 2010-12-22)<br />
* [http://edits.fbk.eu/ EDITS - Edit Distance Textual Entailment Suite] (open source software developed by [http://hlt.fbk.eu/ Human Language Technology (HLT) group at FBK-Irst])<br />
<br />
== RTE data sets ==<br />
* [http://www.coli.uni-saarland.de/projects/salsa/fate FrameNet manually annotated RTE 2006 Test Set.] Provided by [http://www.coli.uni-saarland.de/projects/salsa/ SALSA project, Saarland University.]<br />
* [http://www.cs.biu.ac.il/~nlp/files/RTE_2006_Aligned.zip Manually Word Aligned RTE 2006 Data Sets.] Provided by [http://research.microsoft.com/nlp/ the Natural Language Processing Group, Microsoft Research.]<br />
* [http://www-nlp.stanford.edu/projects/contradiction/ RTE data sets annotated for a 3-way decision: entails, contradicts, unknown.] Provided by Stanford NLP Group.<br />
* [http://www.cs.utexas.edu/~pclark/bpi-test-suite/ BPI RTE data set] - 250 pairs, focusing on world knowledge. Provided jointly by [http://www.boeing.com/phantom/math_ct/index.html Boeing], [http://wordnet.cs.princeton.edu/ Princeton], and [http://www.isi.edu ISI].<br />
* [http://hlt.fbk.eu/en/Technology/TE_Specialized_Data Textual Entailment Specialized Data Sets] - 90 RTE-5 Test Set pairs annotated with linguistic phenomena + 203 monothematic pairs (i.e. pairs where only one linguistic phenomenon is relevant to the entailment relation) created from the 90 annotated pairs. Provided jointly by [http://hlt.fbk.eu/en/home FBK-Irst], and [http://www.celct.it/ CELCT].<br />
* [http://www.nist.gov/tac/data/ RTE-5 Search Pilot Data Set annotated with anaphora and coreference information] - RTE-5 Search Data Set annotated with anaphora/coreference information + Augmented RTE-5 Search Data Set, where all the referring expressions which need to be resolved in the entailing sentences are substituted by explicit expressions on the basis of the anaphora/coreference annotation. Provided by [http://www.celct.it/ CELCT] and distributed by [http://www.nist.gov/index.html NIST] at the [http://www.nist.gov/tac/data/ Past TAC Data] web page (2009 Search Pilot, annotated test/dev data).<br />
* [http://www.investigacion.frc.utn.edu.ar/mslabs/~jcastillo/Sagan-test-suite/ RTE-3-Expanded, RTE-4-Expanded, RTE-5-Expanded.] RTE data set expanded in the two and three way task, at least 2000 pairs in each data set.<br />
<br />
== Knowledge Resources ==<br />
The [[RTE Knowledge Resources]] page presents: <br />
<br />
* a [[RTE Knowledge Resources#Call for Resources|call for resources]], inviting system developers to share the resources used by their own TE engines, to both help improve the TE technology and further test and evaluate such resources;<br />
* [[RTE Knowledge Resources#Ablation tests|the ablation tests]] carried out in the RTE challenges in order to evaluate the impact of knowledge resources and tools on TE system performances;<br />
* [[RTE Knowledge Resources#Publicly available Resources|lists of knowledge resources]], both publically available and unpublished, used by systems participating in the last RTE challenges.<br />
* [https://agora.cs.illinois.edu/display/rtedata/Explanation+Based+Analysis+of+RTE+Data Explanation-Based Analysis annotation of RTE 5 Main Task subset] described in [http://l2r.cs.uiuc.edu/~danr/Papers/SammonsVyRo10.pdf this ACL 2010 paper]<br />
<br />
== Tools ==<br />
<br />
=== Parsers ===<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc C&C parser for Combinatory Categorial Grammar]<br />
* [[Minipar]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SP Shallow Parser] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/shallow_parse_demo.php web demo] of this tool<br />
<br />
=== Role Labelling ===<br />
* [http://cemantix.org/assert ASSERT]<br />
* [http://www.coli.uni-saarland.de/projects/salsa/shal/ Shalmaneser]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SRL Semantic Role Labeler] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/srl-demo.php web demo] of this tool<br />
<br />
=== Entity Recognition Tools ===<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=NE Illinois Named Entity Tagger] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_demo.php web demo] of this tool<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=CORANKER Illinois Multi-lingual Named Entity Discovery Tool] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_matcher_demo.php web demo] of this tool<br />
<br />
=== Similarity / Relatedness Tools ===<br />
* [http://ixa2.si.ehu.es/ukb UKB]: Open source WordNet-based similarity/relatedness tool, includes also pre-computed semantic vectors for all words<br />
<br />
=== Corpus Readers ===<br />
* [http://nltk.org NLTK] provides a corpus reader for the data from RTE Challenges 1, 2, and 3 - see the [http://nltk.org/doc/guides/corpus.html#rte Corpus Readers] Guide for more information.<br />
<br />
=== Related Libraries ===<br />
<br />
* [http://www.semantilog.org/pypes.html PyPES] general purpose library containing evaluation environment for RTE and McPIET text inference engine based on the ERG (English Resource Grammar)<br />
<br />
== Links ==<br />
* [http://homepages.inf.ed.ac.uk/jbos/rte/ Textual Entailment site by Johan Bos]<br />
* [http://ai-nlp.info.uniroma2.it/te/ Textual Entailment at the University of Rome "Tor Vergata"]<br />
[[Category:Textual Entailment Portal]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/entailment-module-demos.php Illinois Textual Entailment System Component demos]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Portal&diff=8576Textual Entailment Portal2010-12-22T12:13:10Z<p>Erel Segal: </p>
<hr />
<div>'''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. <br />
<br />
An example of a positive TE (text entails hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has good consequences''.<br />
<br />
An example of a negative TE (text contradicts hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has no consequences''.<br />
<br />
An example of a non-TE (text does not entail nor contradict) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man will make you better person''.<br />
<br />
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><br />
<br />
Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.<br />
<br />
This page serves as a community portal for everything related to Textual Entailment:<br />
* [[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.<br />
* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]<br />
* [[Textual Entailment References]] - Workshops, Tutorials and Papers.<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_References&diff=8575Textual Entailment References2010-12-22T12:11:18Z<p>Erel Segal: </p>
<hr />
<div>[[Textual Entailment]] &gt; '''References''':<br />
----<br />
<br />
''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).''<br />
<br />
=== Workshops and Tutorials ===<br />
<br />
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]<br />
<br />
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]<br />
<br />
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]<br />
<br />
=== Papers in recent conferences and other workshops ===<br />
<br />
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]<br />
<br />
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]<br />
<br />
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) <br />
<br />
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. <br />
<br />
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications. <br />
RANLP-05.<br />
<br />
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.<br />
<br />
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006<br />
<br />
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.<br />
<br />
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.<br />
<br />
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.<br />
<br />
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).<br />
<br />
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA. <br />
<br />
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05) <br />
<br />
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).<br />
<br />
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.<br />
<br />
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006<br />
<br />
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.<br />
<br />
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.<br />
<br />
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.<br />
<br />
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.<br />
<br />
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). <br />
<br />
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<br />
<br />
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] <br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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).<br />
<br />
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).<br />
<br />
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006<br />
<br />
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006<br />
<br />
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<br />
<br />
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) <br />
<br />
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006. <br />
<br />
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.<br />
<br />
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]<br />
<br />
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.<br />
<br />
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.<br />
<br />
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster). <br />
<br />
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.<br />
<br />
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).<br />
<br />
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.<br />
<br />
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.<br />
<br />
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006<br />
<br />
=== Journal papers ===<br />
<br />
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]<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_References&diff=8574Textual Entailment References2010-12-22T12:10:57Z<p>Erel Segal: </p>
<hr />
<div>[[Textual Entailment]] &gt; '''References'''<br />
----<br />
<br />
''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).''<br />
<br />
=== Workshops and Tutorials ===<br />
<br />
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]<br />
<br />
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]<br />
<br />
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]<br />
<br />
=== Papers in recent conferences and other workshops ===<br />
<br />
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]<br />
<br />
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]<br />
<br />
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) <br />
<br />
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. <br />
<br />
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications. <br />
RANLP-05.<br />
<br />
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.<br />
<br />
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006<br />
<br />
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.<br />
<br />
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.<br />
<br />
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.<br />
<br />
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).<br />
<br />
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA. <br />
<br />
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05) <br />
<br />
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).<br />
<br />
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.<br />
<br />
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006<br />
<br />
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.<br />
<br />
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.<br />
<br />
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.<br />
<br />
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.<br />
<br />
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). <br />
<br />
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<br />
<br />
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] <br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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).<br />
<br />
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).<br />
<br />
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006<br />
<br />
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006<br />
<br />
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<br />
<br />
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) <br />
<br />
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006. <br />
<br />
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.<br />
<br />
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]<br />
<br />
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.<br />
<br />
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.<br />
<br />
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster). <br />
<br />
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.<br />
<br />
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).<br />
<br />
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.<br />
<br />
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.<br />
<br />
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006<br />
<br />
=== Journal papers ===<br />
<br />
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]<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Portal&diff=8573Textual Entailment Portal2010-12-22T12:09:55Z<p>Erel Segal: Moved the references to a seperate page</p>
<hr />
<div>'''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. <br />
<br />
An example of a positive TE (text entails hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has good consequences''.<br />
<br />
An example of a negative TE (text contradicts hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has no consequences''.<br />
<br />
An example of a non-TE (text does not entail nor contradict) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man will make you better person''.<br />
<br />
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><br />
<br />
Recognizing Textual Entailment (RTE) has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications.<br />
<br />
This page serves as a community portal for everything related to Textual Entailment:<br />
* [[Textual Entailment Resource Pool]] - Entailment engines, demos, knowledge resources, etc.<br />
* PASCAL Challenge - [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]]<br />
* [[Textual Entailment References]] - workshops, tutorials and papers.<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_References&diff=8572Textual Entailment References2010-12-22T12:07:29Z<p>Erel Segal: Copied from Textual Entailment</p>
<hr />
<div>[[Textual Entailment]] &gt; '''References''':<br />
<br />
''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).''<br />
<br />
=== Workshops and Tutorials ===<br />
<br />
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]<br />
<br />
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]<br />
<br />
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]<br />
<br />
=== Papers in recent conferences and other workshops ===<br />
<br />
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]<br />
<br />
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]<br />
<br />
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) <br />
<br />
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. <br />
<br />
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications. <br />
RANLP-05.<br />
<br />
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.<br />
<br />
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006<br />
<br />
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.<br />
<br />
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.<br />
<br />
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.<br />
<br />
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).<br />
<br />
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA. <br />
<br />
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05) <br />
<br />
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).<br />
<br />
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.<br />
<br />
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006<br />
<br />
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.<br />
<br />
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.<br />
<br />
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.<br />
<br />
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.<br />
<br />
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). <br />
<br />
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<br />
<br />
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] <br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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).<br />
<br />
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).<br />
<br />
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006<br />
<br />
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006<br />
<br />
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<br />
<br />
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) <br />
<br />
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006. <br />
<br />
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.<br />
<br />
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]<br />
<br />
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.<br />
<br />
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.<br />
<br />
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster). <br />
<br />
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.<br />
<br />
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).<br />
<br />
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.<br />
<br />
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.<br />
<br />
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006<br />
<br />
=== Journal papers ===<br />
<br />
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]<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Resource_Pool&diff=8571Textual Entailment Resource Pool2010-12-22T12:03:24Z<p>Erel Segal: Undo revision 8570 by Erel Segal (Talk)</p>
<hr />
<div>[[Textual Entailment|Textual entailment]] systems rely on many different types of [[Natural Language Processing|NLP]] resources, including term banks, paraphrase lists, parsers, named-entity recognizers, etc. With so many resources being continuously released and improved, it can be difficult to know which particular resource to use when developing a system.<br />
<br />
In response, the [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]] shared task community initiated a new activity for building this ''Textual Entailment Resource Pool''. RTE participants and any other member of the NLP community are encouraged to contribute to the pool.<br />
<br />
In an effort to determine the relative impact of the resources, RTE participants are strongly encouraged to report, whenever possible, the contribution to the overall performance of each utilized resource. Formal qualitative and quantitative results should be included in a separate section of the system report as well as posted on the talk pages of this Textual Entailment Resource Pool.<br />
<br />
'''Adding''' a new resource is very easy. See how to '''use existing templates''' to do this in [[Help:Using Templates]].<br />
<br />
== Complete RTE Systems ==<br />
<br />
* [http://project.cgm.unive.it/html/venses.html VENSES] (from Ca' Foscari University of Venice, Italy)<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc/wiki/nutcracker Nutcracker] (available for download)<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/kindleDemo.php Entailment Demo] (from the University of Illinois at Urbana-Champaign) - INACTIVE (as of 2010-12-22)<br />
* [http://edits.fbk.eu/ EDITS - Edit Distance Textual Entailment Suite] (open source software developed by [http://hlt.fbk.eu/ Human Language Technology (HLT) group at FBK-Irst])<br />
<br />
== RTE data sets ==<br />
* [http://www.coli.uni-saarland.de/projects/salsa/fate FrameNet manually annotated RTE 2006 Test Set.] Provided by [http://www.coli.uni-saarland.de/projects/salsa/ SALSA project, Saarland University.]<br />
* [http://www.cs.biu.ac.il/~nlp/files/RTE_2006_Aligned.zip Manually Word Aligned RTE 2006 Data Sets.] Provided by [http://research.microsoft.com/nlp/ the Natural Language Processing Group, Microsoft Research.]<br />
* [http://www-nlp.stanford.edu/projects/contradiction/ RTE data sets annotated for a 3-way decision: entails, contradicts, unknown.] Provided by Stanford NLP Group.<br />
* [http://www.cs.utexas.edu/~pclark/bpi-test-suite/ BPI RTE data set] - 250 pairs, focusing on world knowledge. Provided jointly by [http://www.boeing.com/phantom/math_ct/index.html Boeing], [http://wordnet.cs.princeton.edu/ Princeton], and [http://www.isi.edu ISI].<br />
* [http://hlt.fbk.eu/en/Technology/TE_Specialized_Data Textual Entailment Specialized Data Sets] - 90 RTE-5 Test Set pairs annotated with linguistic phenomena + 203 monothematic pairs (i.e. pairs where only one linguistic phenomenon is relevant to the entailment relation) created from the 90 annotated pairs. Provided jointly by [http://hlt.fbk.eu/en/home FBK-Irst], and [http://www.celct.it/ CELCT].<br />
* [http://www.nist.gov/tac/data/ RTE-5 Search Pilot Data Set annotated with anaphora and coreference information] - RTE-5 Search Data Set annotated with anaphora/coreference information + Augmented RTE-5 Search Data Set, where all the referring expressions which need to be resolved in the entailing sentences are substituted by explicit expressions on the basis of the anaphora/coreference annotation. Provided by [http://www.celct.it/ CELCT] and distributed by [http://www.nist.gov/index.html NIST] at the [http://www.nist.gov/tac/data/ Past TAC Data] web page (2009 Search Pilot, annotated test/dev data).<br />
* [http://www.investigacion.frc.utn.edu.ar/mslabs/~jcastillo/Sagan-test-suite/ RTE-3-Expanded, RTE-4-Expanded, RTE-5-Expanded.] RTE data set expanded in the two and three way task, at least 2000 pairs in each data set.<br />
<br />
== Knowledge Resources ==<br />
The [[RTE Knowledge Resources]] page presents: <br />
<br />
* a [[RTE Knowledge Resources#Call for Resources|call for resources]], inviting system developers to share the resources used by their own TE engines, to both help improve the TE technology and further test and evaluate such resources;<br />
* [[RTE Knowledge Resources#Ablation tests|the ablation tests]] carried out in the RTE challenges in order to evaluate the impact of knowledge resources and tools on TE system performances;<br />
* [[RTE Knowledge Resources#Publicly available Resources|lists of knowledge resources]], both publically available and unpublished, used by systems participating in the last RTE challenges.<br />
* [https://agora.cs.illinois.edu/display/rtedata/Explanation+Based+Analysis+of+RTE+Data Explanation-Based Analysis annotation of RTE 5 Main Task subset] described in [http://l2r.cs.uiuc.edu/~danr/Papers/SammonsVyRo10.pdf this ACL 2010 paper]<br />
<br />
== Tools ==<br />
<br />
=== Parsers ===<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc C&C parser for Combinatory Categorial Grammar]<br />
* [[Minipar]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SP Shallow Parser] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/shallow_parse_demo.php web demo] of this tool<br />
<br />
=== Role Labelling ===<br />
* [http://cemantix.org/assert ASSERT]<br />
* [http://www.coli.uni-saarland.de/projects/salsa/shal/ Shalmaneser]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SRL Semantic Role Labeler] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/srl-demo.php web demo] of this tool<br />
<br />
=== Entity Recognition Tools ===<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=NE Illinois Named Entity Tagger] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_demo.php web demo] of this tool<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=CORANKER Illinois Multi-lingual Named Entity Discovery Tool] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_matcher_demo.php web demo] of this tool<br />
<br />
=== Similarity / Relatedness Tools ===<br />
* [http://ixa2.si.ehu.es/ukb UKB]: Open source WordNet-based similarity/relatedness tool, includes also pre-computed semantic vectors for all words<br />
<br />
=== Corpus Readers ===<br />
* [http://nltk.org NLTK] provides a corpus reader for the data from RTE Challenges 1, 2, and 3 - see the [http://nltk.org/doc/guides/corpus.html#rte Corpus Readers] Guide for more information.<br />
<br />
=== Related Libraries ===<br />
<br />
* [http://www.semantilog.org/pypes.html PyPES] general purpose library containing evaluation environment for RTE and McPIET text inference engine based on the ERG (English Resource Grammar)<br />
<br />
== Links ==<br />
* [http://homepages.inf.ed.ac.uk/jbos/rte/ Textual Entailment site by Johan Bos]<br />
* [http://ai-nlp.info.uniroma2.it/te/ Textual Entailment at the University of Rome "Tor Vergata"]<br />
[[Category:Textual Entailment Portal]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/entailment-module-demos.php Illinois Textual Entailment System Component demos]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Resource_Pool&diff=8570Textual Entailment Resource Pool2010-12-22T12:01:32Z<p>Erel Segal: /* Knowledge Resources */</p>
<hr />
<div>[[Textual Entailment|Textual entailment]] systems rely on many different types of [[Natural Language Processing|NLP]] resources, including term banks, paraphrase lists, parsers, named-entity recognizers, etc. With so many resources being continuously released and improved, it can be difficult to know which particular resource to use when developing a system.<br />
<br />
In response, the [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]] shared task community initiated a new activity for building this ''Textual Entailment Resource Pool''. RTE participants and any other member of the NLP community are encouraged to contribute to the pool.<br />
<br />
In an effort to determine the relative impact of the resources, RTE participants are strongly encouraged to report, whenever possible, the contribution to the overall performance of each utilized resource. Formal qualitative and quantitative results should be included in a separate section of the system report as well as posted on the talk pages of this Textual Entailment Resource Pool.<br />
<br />
'''Adding''' a new resource is very easy. See how to '''use existing templates''' to do this in [[Help:Using Templates]].<br />
<br />
== Complete RTE Systems ==<br />
<br />
* [http://project.cgm.unive.it/html/venses.html VENSES] (from Ca' Foscari University of Venice, Italy)<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc/wiki/nutcracker Nutcracker] (available for download)<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/kindleDemo.php Entailment Demo] (from the University of Illinois at Urbana-Champaign) - INACTIVE (as of 2010-12-22)<br />
* [http://edits.fbk.eu/ EDITS - Edit Distance Textual Entailment Suite] (open source software developed by [http://hlt.fbk.eu/ Human Language Technology (HLT) group at FBK-Irst])<br />
<br />
== RTE data sets ==<br />
* [http://www.coli.uni-saarland.de/projects/salsa/fate FrameNet manually annotated RTE 2006 Test Set.] Provided by [http://www.coli.uni-saarland.de/projects/salsa/ SALSA project, Saarland University.]<br />
* [http://www.cs.biu.ac.il/~nlp/files/RTE_2006_Aligned.zip Manually Word Aligned RTE 2006 Data Sets.] Provided by [http://research.microsoft.com/nlp/ the Natural Language Processing Group, Microsoft Research.]<br />
* [http://www-nlp.stanford.edu/projects/contradiction/ RTE data sets annotated for a 3-way decision: entails, contradicts, unknown.] Provided by Stanford NLP Group.<br />
* [http://www.cs.utexas.edu/~pclark/bpi-test-suite/ BPI RTE data set] - 250 pairs, focusing on world knowledge. Provided jointly by [http://www.boeing.com/phantom/math_ct/index.html Boeing], [http://wordnet.cs.princeton.edu/ Princeton], and [http://www.isi.edu ISI].<br />
* [http://hlt.fbk.eu/en/Technology/TE_Specialized_Data Textual Entailment Specialized Data Sets] - 90 RTE-5 Test Set pairs annotated with linguistic phenomena + 203 monothematic pairs (i.e. pairs where only one linguistic phenomenon is relevant to the entailment relation) created from the 90 annotated pairs. Provided jointly by [http://hlt.fbk.eu/en/home FBK-Irst], and [http://www.celct.it/ CELCT].<br />
* [http://www.nist.gov/tac/data/ RTE-5 Search Pilot Data Set annotated with anaphora and coreference information] - RTE-5 Search Data Set annotated with anaphora/coreference information + Augmented RTE-5 Search Data Set, where all the referring expressions which need to be resolved in the entailing sentences are substituted by explicit expressions on the basis of the anaphora/coreference annotation. Provided by [http://www.celct.it/ CELCT] and distributed by [http://www.nist.gov/index.html NIST] at the [http://www.nist.gov/tac/data/ Past TAC Data] web page (2009 Search Pilot, annotated test/dev data).<br />
* [http://www.investigacion.frc.utn.edu.ar/mslabs/~jcastillo/Sagan-test-suite/ RTE-3-Expanded, RTE-4-Expanded, RTE-5-Expanded.] RTE data set expanded in the two and three way task, at least 2000 pairs in each data set.<br />
<br />
== Knowledge Resources ==<br />
The [[RTE Knowledge Resources]] page presents: <br />
<br />
* a [[RTE Knowledge Resources#Call for Resources|call for resources]], inviting system developers to share the resources used by their own TE engines, to both help improve the TE technology and further test and evaluate such resources;<br />
* [[RTE Knowledge Resources#Ablation tests|the ablation tests]] carried out in the RTE challenges in order to evaluate the impact of knowledge resources and tools on TE system performances;<br />
* [[RTE Knowledge Resources#Publicly available Resources|lists of knowledge resources]], both publically available and unpublished, used by systems participating in the last RTE challenges.<br />
* [https://agora.cs.illinois.edu/display/rtedata/Explanation+Based+Analysis+of+RTE+Data Explanation-Based Analysis annotation of RTE 5 Main Task subset] described in [http://l2r.cs.uiuc.edu/~danr/Papers/SammonsVyRo10.pdf this ACL 2010 paper]<br />
<br />
Other relevant resources:<br />
* [[WordNet]] - a large English lexical database.<br />
* [[DIRT]] - a collection of paraphrase expressions.<br />
<br />
== Tools ==<br />
<br />
=== Parsers ===<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc C&C parser for Combinatory Categorial Grammar]<br />
* [[Minipar]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SP Shallow Parser] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/shallow_parse_demo.php web demo] of this tool<br />
<br />
=== Role Labelling ===<br />
* [http://cemantix.org/assert ASSERT]<br />
* [http://www.coli.uni-saarland.de/projects/salsa/shal/ Shalmaneser]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SRL Semantic Role Labeler] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/srl-demo.php web demo] of this tool<br />
<br />
=== Entity Recognition Tools ===<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=NE Illinois Named Entity Tagger] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_demo.php web demo] of this tool<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=CORANKER Illinois Multi-lingual Named Entity Discovery Tool] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_matcher_demo.php web demo] of this tool<br />
<br />
=== Similarity / Relatedness Tools ===<br />
* [http://ixa2.si.ehu.es/ukb UKB]: Open source WordNet-based similarity/relatedness tool, includes also pre-computed semantic vectors for all words<br />
<br />
=== Corpus Readers ===<br />
* [http://nltk.org NLTK] provides a corpus reader for the data from RTE Challenges 1, 2, and 3 - see the [http://nltk.org/doc/guides/corpus.html#rte Corpus Readers] Guide for more information.<br />
<br />
=== Related Libraries ===<br />
<br />
* [http://www.semantilog.org/pypes.html PyPES] general purpose library containing evaluation environment for RTE and McPIET text inference engine based on the ERG (English Resource Grammar)<br />
<br />
== Links ==<br />
* [http://homepages.inf.ed.ac.uk/jbos/rte/ Textual Entailment site by Johan Bos]<br />
* [http://ai-nlp.info.uniroma2.it/te/ Textual Entailment at the University of Rome "Tor Vergata"]<br />
[[Category:Textual Entailment Portal]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/entailment-module-demos.php Illinois Textual Entailment System Component demos]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Portal&diff=8569Textual Entailment Portal2010-12-22T11:38:48Z<p>Erel Segal: </p>
<hr />
<div>'''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. <br />
<br />
An example of a positive TE (text entails hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has good consequences''.<br />
<br />
An example of a negative TE (text contradicts hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has no consequences''.<br />
<br />
An example of a non-TE (text does not entail nor contradict) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man will make you better person''.<br />
<br />
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><br />
<br />
This page serves as a community portal for everything related to Textual Entailment. <br />
<br />
== Textual Entailment Resource Pool ==<br />
[[Textual Entailment Resource Pool]]<br />
<br />
== PASCAL Challenges ==<br />
<br />
[[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.<br />
<br />
== References on Textual Entailment ==<br />
''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).''<br />
<br />
=== Workshops and Tutorials ===<br />
<br />
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]<br />
<br />
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]<br />
<br />
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]<br />
<br />
=== Papers in recent conferences and other workshops ===<br />
<br />
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]<br />
<br />
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]<br />
<br />
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) <br />
<br />
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. <br />
<br />
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications. <br />
RANLP-05.<br />
<br />
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.<br />
<br />
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006<br />
<br />
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.<br />
<br />
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.<br />
<br />
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.<br />
<br />
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).<br />
<br />
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA. <br />
<br />
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05) <br />
<br />
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).<br />
<br />
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.<br />
<br />
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006<br />
<br />
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.<br />
<br />
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.<br />
<br />
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.<br />
<br />
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.<br />
<br />
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). <br />
<br />
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<br />
<br />
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] <br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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).<br />
<br />
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).<br />
<br />
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006<br />
<br />
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006<br />
<br />
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<br />
<br />
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) <br />
<br />
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006. <br />
<br />
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.<br />
<br />
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]<br />
<br />
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.<br />
<br />
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.<br />
<br />
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster). <br />
<br />
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.<br />
<br />
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).<br />
<br />
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.<br />
<br />
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.<br />
<br />
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006<br />
<br />
=== Journal papers ===<br />
<br />
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]<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Portal&diff=8568Textual Entailment Portal2010-12-22T11:36:14Z<p>Erel Segal: </p>
<hr />
<div>'''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. <br />
<br />
An example of a positive TE (text entails hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has good consequences''.<br />
<br />
An example of a negative TE (text contradicts hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has no consequences''.<br />
<br />
An example of a non-TE (text does not entail nor contradict) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man will make you better person''.<br />
<br />
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."<br />
<br />
This page serves as a community portal for everything related to Textual Entailment. <br />
<br />
== Textual Entailment Resource Pool ==<br />
[[Textual Entailment Resource Pool]]<br />
<br />
== PASCAL Challenges ==<br />
<br />
[[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.<br />
<br />
== References on Textual Entailment ==<br />
''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).''<br />
<br />
=== Workshops and Tutorials ===<br />
<br />
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]<br />
<br />
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]<br />
<br />
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]<br />
<br />
=== Papers in recent conferences and other workshops ===<br />
<br />
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]<br />
<br />
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]<br />
<br />
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) <br />
<br />
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. <br />
<br />
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications. <br />
RANLP-05.<br />
<br />
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.<br />
<br />
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006<br />
<br />
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.<br />
<br />
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.<br />
<br />
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.<br />
<br />
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).<br />
<br />
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA. <br />
<br />
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05) <br />
<br />
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).<br />
<br />
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.<br />
<br />
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006<br />
<br />
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.<br />
<br />
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.<br />
<br />
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.<br />
<br />
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.<br />
<br />
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). <br />
<br />
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<br />
<br />
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] <br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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).<br />
<br />
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).<br />
<br />
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006<br />
<br />
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006<br />
<br />
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<br />
<br />
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) <br />
<br />
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006. <br />
<br />
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.<br />
<br />
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]<br />
<br />
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.<br />
<br />
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.<br />
<br />
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster). <br />
<br />
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.<br />
<br />
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).<br />
<br />
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.<br />
<br />
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.<br />
<br />
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006<br />
<br />
=== Journal papers ===<br />
<br />
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]<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Portal&diff=8567Textual Entailment Portal2010-12-22T10:59:37Z<p>Erel Segal: definition and examples</p>
<hr />
<div>'''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. <br />
<br />
An example of a positive TE (text entails hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has good consequences''.<br />
<br />
An example of a negative TE (text contradicts hypothesis) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man has no consequences''.<br />
<br />
An example of a non-TE (text does not entail nor contradict) is:<br />
* text: ''If you help the needy, God will reward you''.<br />
* hypothesis: ''Giving money to a poor man will make you better person''.<br />
<br />
This page serves as a community portal for everything related to Textual Entailment. <br />
<br />
== Textual Entailment Resource Pool ==<br />
[[Textual Entailment Resource Pool]]<br />
<br />
== PASCAL Challenges ==<br />
<br />
[[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.<br />
<br />
== References on Textual Entailment ==<br />
''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).''<br />
<br />
=== Workshops and Tutorials ===<br />
<br />
[http://l2r.cs.uiuc.edu/~cogcomp/presentations/RTE_NAACL_2010.zip NAACL 2010 Tutorial on Recognizing Textual Entailment, 2010]<br />
<br />
[http://acl.ldc.upenn.edu/W/W05/#W05-1200 ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE/ First PASCAL Recognising Textual Entailment Challenge (RTE-1), 2005]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE2/ Second PASCAL Recognising Textual Entailment Challenge (RTE-2), 2006]<br />
<br />
[http://nlp.uned.es/QA/ave Answer Validation Exercise at CLEF 2006 (AVE 2006)]<br />
<br />
[http://www.pascal-network.org/Challenges/RTE3/ Third PASCAL Recognising Textual Entailment Challenge (RTE-3), 2007]<br />
<br />
=== Papers in recent conferences and other workshops ===<br />
<br />
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]<br />
<br />
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]<br />
<br />
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) <br />
<br />
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. <br />
<br />
C. Corley, A. Csomai and R. Mihalcea. 2005. Text Semantic Similarity, with Applications. <br />
RANLP-05.<br />
<br />
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.<br />
<br />
I. Dagan, O. Glickman, A. Gliozzo, E. Marmorshtein and C. Strapparava. 2006. Direct Word Sense Matching for Lexical Substitution. COLING-ACL 2006<br />
<br />
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.<br />
<br />
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.<br />
<br />
Georgiana Dinu and Rui Wang. 2009. Inference Rules and their Application to Recognizing Textual Entailment. EACL-09.<br />
<br />
M. Geffet and I. Dagan. 2004. Feature Vector Quality and Distributional Similarity. Proceedings of The 20th International Conference on Computational Linguistics (COLING).<br />
<br />
M. Geffet and I. Dagan. 2005. "The Distributional Inclusion Hypotheses and Lexical Entailment", ACL 2005, Michigan, USA. <br />
<br />
O. Glickman, I. Dagan and M. Koppel. 2005. A Probabilistic Classification Approach for Lexical Textual Entailment, Twentieth National Conference on Artificial Intelligence (AAAI-05) <br />
<br />
O. Glickman, E. Shnarch and I. Dagan. 2006. Lexical Reference: a Semantic Matching Subtask. EMNLP 2006 (poster).<br />
<br />
A. Haghighi, A. Y. Ng, and C. D. Manning. 2005. Robust Textual Inference via Graph Matching. HLT-EMNLP 2005.<br />
<br />
S. Harabagiu and A. Hickl. 2006. Methods for Using Textual Entailment in Open-Domain Question Answering. COLING-ACL 2006<br />
<br />
J. Herrera, A. Peñas, F. Verdejo, 2006. Textual Entailment Recognition Based on Dependency Analysis and WordNet. MLCW 2005. LNAI 3944. 231-239.<br />
<br />
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.<br />
<br />
M. Kouylekov and B. Magnini. 2005. Tree Edit Distance for Textual Entailment. RANLP 2005.<br />
<br />
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.<br />
<br />
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). <br />
<br />
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<br />
<br />
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] <br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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]<br />
<br />
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).<br />
<br />
R. Nairn, C. Condoravdi, and L. Karttunen. 2006. Computing relative polarity for textual inference. International workshop on Inference in Computational Semantics (ICoS-5).<br />
<br />
M. T. Pazienza, M. Pennacchiotti and F. M. Zanzotto . 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. COLING-ACL 2006<br />
<br />
V. Pekar. 2006. Acquisition of Verb Entailment from Text. HLT-NAACL 2006<br />
<br />
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<br />
<br />
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) <br />
<br />
L. Romano, M. Kouylekov, I. Szpektor, I. Dagan and A. Lavelli. 2006. Investigating a Generic Paraphrase-based Approach for Relation Extraction. EACL 2006. <br />
<br />
V. Rus, A. Graesser and K. Desai. 2005. Lexico-Syntactic Subsumption for Textual Entailment. RANLP 2005.<br />
<br />
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]<br />
<br />
R. Snow, L. Vanderwende and A. Menezes. 2006. Effectively Using Syntax for Recognizing False Entialment. HLT-NAACL 2006.<br />
<br />
M. Tatu and D. Moldovan. 2005. A Semantic Approach to Recognizing Textual Entailment. HLT-EMNLP 2005.<br />
<br />
M. Tatu and D. Moldovan. 2006. A Logic-based Semantic Approach to Recognizing Textual Entailment. COLING-ACL 2006 (poster). <br />
<br />
Rui Wang and Günter Neumann. 2007. Recognizing Textual Entailment Using a Subsequence Kernel Method. AAAI-07.<br />
<br />
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).<br />
<br />
Rui Wang and Günter Neumann. 2009. An Accuracy-Oriented Divide-and-Conquer Strategy for Recognizing Textual Entailment. TAC 2008 Workshop - RTE-4.<br />
<br />
Rui Wang and Yi Zhang. 2009. Recognizing Textual Relatedness with Predicate-Argument Structures. EMNLP 2009.<br />
<br />
F. M. Zanzotto and A. Moschitti. 2006. Automatic learning of textual entailments with cross-pair similarities. COLING-ACL 2006<br />
<br />
=== Journal papers ===<br />
<br />
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]<br />
<br />
[[Category:Textual Entailment Portal]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Textual_Entailment_Resource_Pool&diff=8566Textual Entailment Resource Pool2010-12-22T10:51:12Z<p>Erel Segal: </p>
<hr />
<div>[[Textual Entailment|Textual entailment]] systems rely on many different types of [[Natural Language Processing|NLP]] resources, including term banks, paraphrase lists, parsers, named-entity recognizers, etc. With so many resources being continuously released and improved, it can be difficult to know which particular resource to use when developing a system.<br />
<br />
In response, the [[Recognizing Textual Entailment|Recognizing Textual Entailment (RTE)]] shared task community initiated a new activity for building this ''Textual Entailment Resource Pool''. RTE participants and any other member of the NLP community are encouraged to contribute to the pool.<br />
<br />
In an effort to determine the relative impact of the resources, RTE participants are strongly encouraged to report, whenever possible, the contribution to the overall performance of each utilized resource. Formal qualitative and quantitative results should be included in a separate section of the system report as well as posted on the talk pages of this Textual Entailment Resource Pool.<br />
<br />
'''Adding''' a new resource is very easy. See how to '''use existing templates''' to do this in [[Help:Using Templates]].<br />
<br />
== Complete RTE Systems ==<br />
<br />
* [http://project.cgm.unive.it/html/venses.html VENSES] (from Ca' Foscari University of Venice, Italy)<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc/wiki/nutcracker Nutcracker] (available for download)<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/kindleDemo.php Entailment Demo] (from the University of Illinois at Urbana-Champaign) - INACTIVE (as of 2010-12-22)<br />
* [http://edits.fbk.eu/ EDITS - Edit Distance Textual Entailment Suite] (open source software developed by [http://hlt.fbk.eu/ Human Language Technology (HLT) group at FBK-Irst])<br />
<br />
== RTE data sets ==<br />
* [http://www.coli.uni-saarland.de/projects/salsa/fate FrameNet manually annotated RTE 2006 Test Set.] Provided by [http://www.coli.uni-saarland.de/projects/salsa/ SALSA project, Saarland University.]<br />
* [http://www.cs.biu.ac.il/~nlp/files/RTE_2006_Aligned.zip Manually Word Aligned RTE 2006 Data Sets.] Provided by [http://research.microsoft.com/nlp/ the Natural Language Processing Group, Microsoft Research.]<br />
* [http://www-nlp.stanford.edu/projects/contradiction/ RTE data sets annotated for a 3-way decision: entails, contradicts, unknown.] Provided by Stanford NLP Group.<br />
* [http://www.cs.utexas.edu/~pclark/bpi-test-suite/ BPI RTE data set] - 250 pairs, focusing on world knowledge. Provided jointly by [http://www.boeing.com/phantom/math_ct/index.html Boeing], [http://wordnet.cs.princeton.edu/ Princeton], and [http://www.isi.edu ISI].<br />
* [http://hlt.fbk.eu/en/Technology/TE_Specialized_Data Textual Entailment Specialized Data Sets] - 90 RTE-5 Test Set pairs annotated with linguistic phenomena + 203 monothematic pairs (i.e. pairs where only one linguistic phenomenon is relevant to the entailment relation) created from the 90 annotated pairs. Provided jointly by [http://hlt.fbk.eu/en/home FBK-Irst], and [http://www.celct.it/ CELCT].<br />
* [http://www.nist.gov/tac/data/ RTE-5 Search Pilot Data Set annotated with anaphora and coreference information] - RTE-5 Search Data Set annotated with anaphora/coreference information + Augmented RTE-5 Search Data Set, where all the referring expressions which need to be resolved in the entailing sentences are substituted by explicit expressions on the basis of the anaphora/coreference annotation. Provided by [http://www.celct.it/ CELCT] and distributed by [http://www.nist.gov/index.html NIST] at the [http://www.nist.gov/tac/data/ Past TAC Data] web page (2009 Search Pilot, annotated test/dev data).<br />
* [http://www.investigacion.frc.utn.edu.ar/mslabs/~jcastillo/Sagan-test-suite/ RTE-3-Expanded, RTE-4-Expanded, RTE-5-Expanded.] RTE data set expanded in the two and three way task, at least 2000 pairs in each data set.<br />
<br />
== Knowledge Resources ==<br />
The [[RTE Knowledge Resources]] page presents: <br />
<br />
* a [[RTE Knowledge Resources#Call for Resources|call for resources]], inviting system developers to share the resources used by their own TE engines, to both help improve the TE technology and further test and evaluate such resources;<br />
* [[RTE Knowledge Resources#Ablation tests|the ablation tests]] carried out in the RTE challenges in order to evaluate the impact of knowledge resources and tools on TE system performances;<br />
* [[RTE Knowledge Resources#Publicly available Resources|lists of knowledge resources]], both publically available and unpublished, used by systems participating in the last RTE challenges.<br />
* [https://agora.cs.illinois.edu/display/rtedata/Explanation+Based+Analysis+of+RTE+Data Explanation-Based Analysis annotation of RTE 5 Main Task subset] described in [http://l2r.cs.uiuc.edu/~danr/Papers/SammonsVyRo10.pdf this ACL 2010 paper]<br />
<br />
== Tools ==<br />
<br />
=== Parsers ===<br />
* [http://svn.ask.it.usyd.edu.au/trac/candc C&C parser for Combinatory Categorial Grammar]<br />
* [[Minipar]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SP Shallow Parser] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/shallow_parse_demo.php web demo] of this tool<br />
<br />
=== Role Labelling ===<br />
* [http://cemantix.org/assert ASSERT]<br />
* [http://www.coli.uni-saarland.de/projects/salsa/shal/ Shalmaneser]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=SRL Semantic Role Labeler] - from the University of Illinois at Urbana-Champaign, see a [http://l2r.cs.uiuc.edu/~cogcomp/srl-demo.php web demo] of this tool<br />
<br />
=== Entity Recognition Tools ===<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=NE Illinois Named Entity Tagger] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_demo.php web demo] of this tool<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/asoftware.php?skey=CORANKER Illinois Multi-lingual Named Entity Discovery Tool] - see a [http://l2r.cs.uiuc.edu/~cogcomp/ne_matcher_demo.php web demo] of this tool<br />
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=== Similarity / Relatedness Tools ===<br />
* [http://ixa2.si.ehu.es/ukb UKB]: Open source WordNet-based similarity/relatedness tool, includes also pre-computed semantic vectors for all words<br />
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=== Corpus Readers ===<br />
* [http://nltk.org NLTK] provides a corpus reader for the data from RTE Challenges 1, 2, and 3 - see the [http://nltk.org/doc/guides/corpus.html#rte Corpus Readers] Guide for more information.<br />
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=== Related Libraries ===<br />
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* [http://www.semantilog.org/pypes.html PyPES] general purpose library containing evaluation environment for RTE and McPIET text inference engine based on the ERG (English Resource Grammar)<br />
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== Links ==<br />
* [http://homepages.inf.ed.ac.uk/jbos/rte/ Textual Entailment site by Johan Bos]<br />
* [http://ai-nlp.info.uniroma2.it/te/ Textual Entailment at the University of Rome "Tor Vergata"]<br />
[[Category:Textual Entailment Portal]]<br />
* [http://l2r.cs.uiuc.edu/~cogcomp/entailment-module-demos.php Illinois Textual Entailment System Component demos]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=Research&diff=8561Research2010-12-21T13:05:13Z<p>Erel Segal: /* ACL Wiki articles and tutorials */</p>
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<div>This page is a list of links to information on research in Computational Linguistics.<br />
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* [http://www.aclweb.org/anthology ACL Anthology] - more than 10,000 CL papers<br />
* [[Bibliographies]]<br />
* [[Books]]<br />
* [[Formalisms]]<br />
* [[Papers]]<br />
* [[Resources]]<br />
* [[Wikipedia articles]] - on topics related to Computational Linguistics<br />
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== ACL Wiki articles and tutorials ==<br />
Write your own article or tutorial!<br />
<!-- Please keep this list in alphabetical order --><br />
* [[Active Learning for NLP]] (stub)<br />
* [[Computational Lexicology]]<br />
* [[Computational Morphology]] (stub)<br />
* [[Computational Phonology]]<br />
* [[Computational Semantics]]<br />
* [[Computational Syntax]]<br />
* [[Constrained Conditional Model]] (stub)<br />
* [[Dialectometrics]]<br />
* [[Dialogue Systems]] (stub)<br />
* [[Distributional Hypothesis]]<br />
* [[Graph Based Methods]] (stub)<br />
* [[Information Extraction]] (stub)<br />
* [[Lexical Acquisition]] (stub)<br />
* [[Machine Translation]] (stub)<br />
* [[Natural Language Generation Portal]]<br />
* [[Natural Language Understanding]] (redirect)<br />
* [[Multiword Expressions]] (stub)<br />
* [[Parsing]] (stub)<br />
* [[Part-of-speech tagging]]<br />
* [[Question Answering]]<br />
* [[Semantics]] (stub)<br />
* [[Speech Processing]]<br />
* [[Statistical Semantics]]<br />
* [[Text Categorization]]<br />
* [[Textual Entailment]]<br />
* [[Text Summarization]] (stub)<br />
* [[Word Sense Disambiguation]]<br />
<!-- Please keep this list in alphabetical order --><br />
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[[Category:Research|*]]</div>Erel Segalhttps://aclweb.org/aclwiki/index.php?title=User:Erel_Segal&diff=8560User:Erel Segal2010-12-21T12:33:31Z<p>Erel Segal: New page: [http://he.wikisource.org/wiki/%D7%9E%D7%A9%D7%AA%D7%9E%D7%A9:Erel_Segal My main profile] in Hebrew Wikisource;</p>
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<div>[http://he.wikisource.org/wiki/%D7%9E%D7%A9%D7%AA%D7%9E%D7%A9:Erel_Segal My main profile] in Hebrew Wikisource;</div>Erel Segal