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Reseach Article

Different Models and Approaches of Textual Entailment Recognition

by Mohamed H. Haggag, Marwa M.A. ELFattah, Ahmed Mohammed Ahmed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 1
Year of Publication: 2016
Authors: Mohamed H. Haggag, Marwa M.A. ELFattah, Ahmed Mohammed Ahmed
10.5120/ijca2016909667

Mohamed H. Haggag, Marwa M.A. ELFattah, Ahmed Mohammed Ahmed . Different Models and Approaches of Textual Entailment Recognition. International Journal of Computer Applications. 142, 1 ( May 2016), 32-39. DOI=10.5120/ijca2016909667

@article{ 10.5120/ijca2016909667,
author = { Mohamed H. Haggag, Marwa M.A. ELFattah, Ahmed Mohammed Ahmed },
title = { Different Models and Approaches of Textual Entailment Recognition },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 1 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number1/24863-2016909667/ },
doi = { 10.5120/ijca2016909667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:48.110118+05:30
%A Mohamed H. Haggag
%A Marwa M.A. ELFattah
%A Ahmed Mohammed Ahmed
%T Different Models and Approaches of Textual Entailment Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 1
%P 32-39
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Variability of semantic expression is a fundamental phenomenon of a natural language where same meaning can be expressed by different texts. The process of inferring a text from another is called textual entailment. Textual Entailment is useful in a wide range of applications, including question answering, summarization, text generation, and machine translation. The recognition of textual entailment is one of the recent challenges of the Natural Language Processing (NLP) domain. This paper summarizes key ideas from the area of textual entailment recognition by considering in turn the different recognition models. The paper points to prominent testing data, training data, resources and Performance Evaluation for each model. Also this paper compares between textual entailment models according to the method which used, the result of each method and the strong and weakness of each method.

References
  1. Russell, Stuart and P.Norvig. “Artificial Intelligence: A Modern Approach”. Prentice-Hall, Englewood Cliffs, NJ.1995
  2. S.Gautam , “Text Entailment and Machine Translation Evaluation” , Center for Indian Language Technology (CFILT) ,2014
  3. I.Androutsopoulos , P. Malakasiotis , “A Survey of Paraphrasing and Textual Entailment Methods”, Journal of Artificial Intelligence Research 38 (2010) 135-187 , Submitted 12/09; published 05/10.
  4. I.DAGAN, B.DOLAN, B.MAGNINI and D.ROTH , “Recognizing textual entailment: Rational, evaluation and approaches” – Erratum , Natural Language Engineering / Volume 16 / Issue 01 / January 2010, pp 105 – 105 DOI: 10.1017/S1351324909990234, Published online: 28 January 2010 .
  5. D. Majumdar , P.Bhattacharyya ,” Lexical Based Text Entailment System for Main Task of RTE6” , Text Analysis Conference (TAC) Proceedings ,2010
  6. E.Lien , “Using Minimal Recursion Semantics for Entailment Recognition “, Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 76–84, Gothenburg, Sweden, April 26-30 2014. c 2014 Association for Computational Linguistics.
  7. Z.Zhang, D.Yao, S.Chen, and H.Ma , “Chinese Textual Entailment Recognition Based on Syntactic Tree Clipping” , M. Sun et al. (Eds.): CCL and NLP-NABD 2014, LNAI 8801, pp. 83–94, 2014.© Springer International Publishing Switzerland 2014.
  8. M.Rios, L.Specia, A.Gelbukh, and R.Mitkov, “Statistical Relational Learning to Recognise Textual Entailment” , A. Gelbukh (Ed.): CICLing 2014, Part I, LNCS 8403, pp. 330–339, 2014,_c Springer-Verlag Berlin Heidelberg 2014.
  9. P.Pakray, S.Bandyopadhyay and A.Gelbukh, “TEXTUAL ENTAILMENT USING LEXICAL AND SYNTACTIC SIMILARITY” , International Journal of Artificial Intelligence & Applications (IJAIA), Vol.2, No.1, January 2011 .
  10. R.Mei , X. Li , “A Hybrid Approach to Textual Entailment Recognition” , International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 2014).
  11. M. Tatu, B. Iles, J. Slavick, A. Novischi, and D. Moldovan.” Cogex at the second recognizing textual entailment challenge”. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, pages 104{109, Venice, Italy, April 2006.
  12. R.de Salvo Braz, R. Girju, V. Punyakanok, D. Roth, and M. Sammons. “An inference model for semantic entailment in natural language”. Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment, pages 261{286, 2006.
  13. P. Clark and P. Harrison. “Recognizing textual entailment with logical inference”. In Proceedings of the 2008 Text Analysis Conference (TAC'08), Gaithersburg, Maryland, USA, November 2008.
  14. R.Wang and G.Neumann.” A divide-and-conquer strategy for recognizing textual entailment”. Text Analysis Conference (TAC'08), Gaithersburg, Maryland, USA, November 2008.
  15. J.Bos and K.Markert. “When logical inference helps determining textual entailment” (and when it doesn't). In Proceedings of the Second PASCAL RTE Challenge, Venice, Italy, April 2006.
  16. A.Riazanov and A.Voronkov. “The design and implementation of vampire”. AI communications, 15(2):91{110, 2002.
  17. R. Adams. "Textual entailment through extended lexical overlap”. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, pages 128{133, Venice, Italy, April 2006.
  18. G. Hirst and D. St-Onge. “Lexical chains as representations of context for the detection and correction of malapropisms”. WordNet An electronic lexical database, pages 305{332, April 1998.
  19. A. Iftene. “UAIC Participation at RTE4”. Text Analysis Conference (TAC'08), Gaithersburg, Maryland, USA, November 2008.
  20. D.Lin. “Dependency-based evaluation of minipar”. Treebanks, pages 317{329, 2003.
  21. F.M. Zanzotto, A. Moschitti, M. Pennacchiotti, and M.T. Pazienza. “Learning textual entailment from examples”. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy, April 2006.
  22. M.C. de Marne_e, B. MacCartney, T. Grenager, D. Cer, A. Ra_erty, and C.D. “Manning. Learning to distinguish valid textual entailments”. In Proceedings of the Second PASCAL RTE Challenge Workshop, Venice, Italy, April 2006.
  23. R. Bar-Haim, J. Berant, and I. Dagan. “A compact forest for scalable inference over entailment and paraphrase rules”. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3, pages 1056{1065, Singapore, August 2009. Association for Computational Linguistics.
  24. M. Sammons, V.G.V. Vydiswaran, T. Vieira, N. Johri, M.W. Chang, D. Goldwasser, V. Srikumar, G. Kundu, Y. Tu, K. Small, et al. Relation “alignment for textual entailment recognition”. In Proceedings of the 2009 Text Analysis Conference (TAC'09), Gaithersburg, Maryland, USA, November 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Text entailment recognition WordNet Semantic analysis. Data Mining