This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally decomposable matching score. While this formulation is adequate for representing local (word-level) phenomena such as synonymy, it is incapable of representing global interactions, such as that between verb negation and the addition/removal of qualifiers, which are often critical for determining entailment. We propose a pipelined approach where alignment is followed by a classification step, in which we extract features representing high-level characteristics of the entailment problem, and give the resulting feature vector to a statistical classifier trained on development data.
%0 Conference Paper
%1 marneffe06learning
%A de Marneffe, Marie-Catherine
%A MacCartney, Bill
%A Grenager, Trond
%A Cer, Daniel
%A Rafferty, Anna
%A Manning, Christopher D.
%B Proceedings of the Second PASCAL Challenges Workshop
%D 2006
%K 2006 stanford parsetree parser NT2OD nlp
%T Learning to distinguish valid textual entailments
%U http://nlp.stanford.edu/pubs/rte2-report.pdf
%X This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally decomposable matching score. While this formulation is adequate for representing local (word-level) phenomena such as synonymy, it is incapable of representing global interactions, such as that between verb negation and the addition/removal of qualifiers, which are often critical for determining entailment. We propose a pipelined approach where alignment is followed by a classification step, in which we extract features representing high-level characteristics of the entailment problem, and give the resulting feature vector to a statistical classifier trained on development data.
@inproceedings{marneffe06learning,
abstract = {This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally decomposable matching score. While this formulation is adequate for representing local (word-level) phenomena such as synonymy, it is incapable of representing global interactions, such as that between verb negation and the addition/removal of qualifiers, which are often critical for determining entailment. We propose a pipelined approach where alignment is followed by a classification step, in which we extract features representing high-level characteristics of the entailment problem, and give the resulting feature vector to a statistical classifier trained on development data.},
added-at = {2007-02-25T19:17:08.000+0100},
author = {de Marneffe, Marie-Catherine and MacCartney, Bill and Grenager, Trond and Cer, Daniel and Rafferty, Anna and Manning, Christopher D.},
biburl = {https://www.bibsonomy.org/bibtex/29a177d866b0ebfc4d04bc447d680707f/butonic},
booktitle = {Proceedings of the Second PASCAL Challenges Workshop},
interhash = {0f4b7670820b8b58e156e740ac34b251},
intrahash = {9a177d866b0ebfc4d04bc447d680707f},
keywords = {2006 stanford parsetree parser NT2OD nlp},
organization = {The Stanford Natural Language Processing Group},
school = {Stanford University},
timestamp = {2007-02-25T19:17:08.000+0100},
title = {Learning to distinguish valid textual entailments},
url = {http://nlp.stanford.edu/pubs/rte2-report.pdf},
year = 2006
}