Multi-word expressions (MWE) have seen much attention from the NLP community. In this paper, we investigate their impact on the recognition of textual entailment (RTE). Using the manual Microsoft Research annotations, we first manually count and classify MWEs in RTE data. We find few, most of which are arguably unlikely to cause processing problems. We then consider the impact of MWEs on a current RTE system. We are unable to confirm that entailment recognition suffers from wrongly aligned MWEs. In addition, MWE alignment is difficult to improve, since MWEs are poorly represented in state-of-the-art paraphrase resources, the only available sources for multi-word similarities. We conclude that RTE should concentrate on other phenomena impacting entailment, and that paraphrase knowledge is best understood as capturing general lexico-syntactic variation.
%0 Conference Paper
%1 marneffe09:_multi_word_expres_in_textual_entail
%A de Marneffe, Marie-Catherine
%A Padó, Sebastian
%A Manning, Christopher D.
%B Proceedings of the ACL TextInfer workshop
%C Singapore
%D 2009
%K myown workshop
%P 1--9
%T Multi-word expressions in Textual Entailment: Much ado about nothing?
%U http://aclweb.org/anthology/W/W09/W09-2501.pdf
%X Multi-word expressions (MWE) have seen much attention from the NLP community. In this paper, we investigate their impact on the recognition of textual entailment (RTE). Using the manual Microsoft Research annotations, we first manually count and classify MWEs in RTE data. We find few, most of which are arguably unlikely to cause processing problems. We then consider the impact of MWEs on a current RTE system. We are unable to confirm that entailment recognition suffers from wrongly aligned MWEs. In addition, MWE alignment is difficult to improve, since MWEs are poorly represented in state-of-the-art paraphrase resources, the only available sources for multi-word similarities. We conclude that RTE should concentrate on other phenomena impacting entailment, and that paraphrase knowledge is best understood as capturing general lexico-syntactic variation.
@inproceedings{marneffe09:_multi_word_expres_in_textual_entail,
abstract = {Multi-word expressions (MWE) have seen much attention from the NLP community. In this paper, we investigate their impact on the recognition of textual entailment (RTE). Using the manual Microsoft Research annotations, we first manually count and classify MWEs in RTE data. We find few, most of which are arguably unlikely to cause processing problems. We then consider the impact of MWEs on a current RTE system. We are unable to confirm that entailment recognition suffers from wrongly aligned MWEs. In addition, MWE alignment is difficult to improve, since MWEs are poorly represented in state-of-the-art paraphrase resources, the only available sources for multi-word similarities. We conclude that RTE should concentrate on other phenomena impacting entailment, and that paraphrase knowledge is best understood as capturing general lexico-syntactic variation.},
added-at = {2017-04-03T19:29:52.000+0200},
address = {Singapore},
author = {de Marneffe, Marie-Catherine and Pad\'o, Sebastian and Manning, Christopher D.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2f895353b8b7b945219949de76ccfc5ef/sp},
booktitle = {Proceedings of the ACL TextInfer workshop},
interhash = {20e5aa94293770035da4b7deb7a0df24},
intrahash = {f895353b8b7b945219949de76ccfc5ef},
keywords = {myown workshop},
pages = {1--9},
timestamp = {2017-04-03T17:30:49.000+0200},
title = {Multi-word expressions in Textual Entailment: Much ado about nothing?},
url = {http://aclweb.org/anthology/W/W09/W09-2501.pdf},
year = 2009
}
