A study of textual entailment

Vasile Rus; Philip M. McCarthy; Danielle S. McNamara; Arthur C. Graesser

doi:10.1142/S0218213008004096

A study of textual entailment

Vasile Rus, Philip M. McCarthy, Danielle S. McNamara, Arthur C. Graesser

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

In this paper we study a graph-based approach to the task of Recognizing Textual Entailment between a Text and a Hypothesis. The approach takes into account the full lexico-syntactic context of both the Text and Hypothesis and is based on the concept of subsumption. It starts with mapping the Text and Hypothesis on to graph structures that have nodes representing concepts and edges representing lexico-syntactic relations among concepts. An entailment decision is then made on the basis of a subsumption score between the Text-graph and Hypothesis-graph. The results obtained from a standard entailment test data set were promising. The impact of synonymy on entailment is quantified and discussed. An important advantage to a solution like ours is its ability to be customized to obtain high-confidence results.

Original language	English (US)
Pages (from-to)	659-685
Number of pages	27
Journal	International Journal on Artificial Intelligence Tools
Volume	17
Issue number	4
DOIs	https://doi.org/10.1142/S0218213008004096
State	Published - Aug 2008
Externally published	Yes

Keywords

Graph subsumption
Natural language processing
Syntactic dependencies
Textual entailment

ASJC Scopus subject areas

Artificial Intelligence

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10.1142/S0218213008004096

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title = "A study of textual entailment",

abstract = "In this paper we study a graph-based approach to the task of Recognizing Textual Entailment between a Text and a Hypothesis. The approach takes into account the full lexico-syntactic context of both the Text and Hypothesis and is based on the concept of subsumption. It starts with mapping the Text and Hypothesis on to graph structures that have nodes representing concepts and edges representing lexico-syntactic relations among concepts. An entailment decision is then made on the basis of a subsumption score between the Text-graph and Hypothesis-graph. The results obtained from a standard entailment test data set were promising. The impact of synonymy on entailment is quantified and discussed. An important advantage to a solution like ours is its ability to be customized to obtain high-confidence results.",

keywords = "Graph subsumption, Natural language processing, Syntactic dependencies, Textual entailment",

author = "Vasile Rus and McCarthy, {Philip M.} and McNamara, {Danielle S.} and Graesser, {Arthur C.}",

note = "Funding Information: This research was partially funded by The University of Memphis and AutoTutor, Coh-Metrix, and iSTART projects. The research on AutoTutor was supported by the National Science Foundation (REC 106965, ITR 0325428). The Coh-Metrix project was funded by Institute for Education Sciences (IES R305G020018-02). The iSTART project was funded by the Institute for Education Sciences (IES R305G040046). Any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of The University of Memphis, NSF, or IES. We are also grateful to five anonymous reviewers for their valuable comments.",

year = "2008",

month = aug,

doi = "10.1142/S0218213008004096",

language = "English (US)",

volume = "17",

pages = "659--685",

journal = "International Journal on Artificial Intelligence Tools",

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publisher = "World Scientific Publishing Co. Pte Ltd",

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AU - Rus, Vasile

AU - McCarthy, Philip M.

AU - McNamara, Danielle S.

AU - Graesser, Arthur C.

N1 - Funding Information: This research was partially funded by The University of Memphis and AutoTutor, Coh-Metrix, and iSTART projects. The research on AutoTutor was supported by the National Science Foundation (REC 106965, ITR 0325428). The Coh-Metrix project was funded by Institute for Education Sciences (IES R305G020018-02). The iSTART project was funded by the Institute for Education Sciences (IES R305G040046). Any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of The University of Memphis, NSF, or IES. We are also grateful to five anonymous reviewers for their valuable comments.

PY - 2008/8

Y1 - 2008/8

N2 - In this paper we study a graph-based approach to the task of Recognizing Textual Entailment between a Text and a Hypothesis. The approach takes into account the full lexico-syntactic context of both the Text and Hypothesis and is based on the concept of subsumption. It starts with mapping the Text and Hypothesis on to graph structures that have nodes representing concepts and edges representing lexico-syntactic relations among concepts. An entailment decision is then made on the basis of a subsumption score between the Text-graph and Hypothesis-graph. The results obtained from a standard entailment test data set were promising. The impact of synonymy on entailment is quantified and discussed. An important advantage to a solution like ours is its ability to be customized to obtain high-confidence results.

AB - In this paper we study a graph-based approach to the task of Recognizing Textual Entailment between a Text and a Hypothesis. The approach takes into account the full lexico-syntactic context of both the Text and Hypothesis and is based on the concept of subsumption. It starts with mapping the Text and Hypothesis on to graph structures that have nodes representing concepts and edges representing lexico-syntactic relations among concepts. An entailment decision is then made on the basis of a subsumption score between the Text-graph and Hypothesis-graph. The results obtained from a standard entailment test data set were promising. The impact of synonymy on entailment is quantified and discussed. An important advantage to a solution like ours is its ability to be customized to obtain high-confidence results.

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KW - Natural language processing

KW - Syntactic dependencies

KW - Textual entailment

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A study of textual entailment

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Keywords

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