A lexical alignment model for probabilistic textual entailment

Oren Glickman; Ido Dagan; Moshe Koppel

doi:10.1007/11736790_16

A lexical alignment model for probabilistic textual entailment

Oren Glickman, Ido Dagan, Moshe Koppel

Department of Computer Science

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

14 Scopus citations

Abstract

This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the Recognising Textual Entailment challenge dataset along with some analysis.

Original language	English
Title of host publication	Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.
Publisher	Springer Verlag
Pages	287-298
Number of pages	12
ISBN (Print)	3540334270, 9783540334279
DOIs	https://doi.org/10.1007/11736790_16
State	Published - 2006
Event	1st PASCAL Machine Learning Challenges Workshop, MLCW 2005 - Southampton, United Kingdom Duration: 11 Apr 2005 → 13 Apr 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3944 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st PASCAL Machine Learning Challenges Workshop, MLCW 2005
Country/Territory	United Kingdom
City	Southampton
Period	11/04/05 → 13/04/05

Access to Document

10.1007/11736790_16

Cite this

Glickman, O., Dagan, I., & Koppel, M. (2006). A lexical alignment model for probabilistic textual entailment. In Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap. (pp. 287-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3944 LNAI). Springer Verlag. https://doi.org/10.1007/11736790_16

Glickman, Oren ; Dagan, Ido ; Koppel, Moshe. / A lexical alignment model for probabilistic textual entailment. Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. Springer Verlag, 2006. pp. 287-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the Recognising Textual Entailment challenge dataset along with some analysis.",

author = "Oren Glickman and Ido Dagan and Moshe Koppel",

year = "2006",

doi = "10.1007/11736790_16",

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Glickman, O , Dagan, I & Koppel, M 2006, A lexical alignment model for probabilistic textual entailment. in Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3944 LNAI, Springer Verlag, pp. 287-298, 1st PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, United Kingdom, 11/04/05. https://doi.org/10.1007/11736790_16

A lexical alignment model for probabilistic textual entailment. / Glickman, Oren ; Dagan, Ido ; Koppel, Moshe.
Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. Springer Verlag, 2006. p. 287-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3944 LNAI).

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

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Glickman O , Dagan I , Koppel M. A lexical alignment model for probabilistic textual entailment. In Machine Learning Challenges - Evaluating Predictive Uncertainty, Visual Object Classification, and Recog. Textual Entailment - First PASCAL Machine Learn. Challenges Workshop, MLCW 2005, Revised Pap.. Springer Verlag. 2006. p. 287-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11736790_16

A lexical alignment model for probabilistic textual entailment

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