Recognizing textual entailment using a machine learning approach

Miguel Angel Ríos Gaona; Alexander Gelbukh; Sivaji Bandyopadhyay

doi:10.1007/978-3-642-16773-7_15

Recognizing textual entailment using a machine learning approach

Miguel Angel Ríos Gaona, Alexander Gelbukh, Sivaji Bandyopadhyay

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset.

Original language	English
Title of host publication	Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings
Pages	177-185
Number of pages	9
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-16773-7_15
State	Published - 2010
Event	9th Mexican International Conference on Artificial Intelligence, MICAI 2010 - Pachuca, Mexico Duration: 8 Nov 2010 → 13 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6438 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Mexican International Conference on Artificial Intelligence, MICAI 2010
Country/Territory	Mexico
City	Pachuca
Period	8/11/10 → 13/11/10

Keywords

Recognizing Textual Entailment
non-symmetric measures
text similarity measures

Access to Document

10.1007/978-3-642-16773-7_15

Cite this

Ríos Gaona, M. A., Gelbukh, A., & Bandyopadhyay, S. (2010). Recognizing textual entailment using a machine learning approach. In Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings (PART 2 ed., pp. 177-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6438 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-16773-7_15

Ríos Gaona, Miguel Angel ; Gelbukh, Alexander ; Bandyopadhyay, Sivaji. / Recognizing textual entailment using a machine learning approach. Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings. PART 2. ed. 2010. pp. 177-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset.",

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author = "{R{\'i}os Gaona}, {Miguel Angel} and Alexander Gelbukh and Sivaji Bandyopadhyay",

note = "Funding Information: Acknowledgements. The work was done under partial support of Mexican Government (SNI, CONACYT grant 50206-H, CONACYT scholarship for Sabbatical stay at Waseda U., COFAA-IPN, and SIP-IPN grant 20100773) and Government of India (CONACYT-DST India funded project).; 9th Mexican International Conference on Artificial Intelligence, MICAI 2010 ; Conference date: 08-11-2010 Through 13-11-2010",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Ríos Gaona, MA, Gelbukh, A & Bandyopadhyay, S 2010, Recognizing textual entailment using a machine learning approach. in Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6438 LNAI, pp. 177-185, 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Pachuca, Mexico, 8/11/10. https://doi.org/10.1007/978-3-642-16773-7_15

Recognizing textual entailment using a machine learning approach. / Ríos Gaona, Miguel Angel; Gelbukh, Alexander; Bandyopadhyay, Sivaji.
Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings. PART 2. ed. 2010. p. 177-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6438 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Funding Information: Acknowledgements. The work was done under partial support of Mexican Government (SNI, CONACYT grant 50206-H, CONACYT scholarship for Sabbatical stay at Waseda U., COFAA-IPN, and SIP-IPN grant 20100773) and Government of India (CONACYT-DST India funded project).

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Ríos Gaona MA, Gelbukh A, Bandyopadhyay S. Recognizing textual entailment using a machine learning approach. In Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings. PART 2 ed. 2010. p. 177-185. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-16773-7_15

Recognizing textual entailment using a machine learning approach

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