Learning antonyms with paraphrases and a morphology-aware neural network

Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki, Vered Shwartz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.

Original languageEnglish
Title of host publication*SEM 2017 - 6th Joint Conference on Lexical and Computational Semantics, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages12-21
Number of pages10
ISBN (Electronic)9781945626531
DOIs
StatePublished - 2017
Event6th Joint Conference on Lexical and Computational Semantics, *SEM 2017 - Vancouver, Canada
Duration: 3 Aug 20174 Aug 2017

Publication series

Name*SEM 2017 - 6th Joint Conference on Lexical and Computational Semantics, Proceedings

Conference

Conference6th Joint Conference on Lexical and Computational Semantics, *SEM 2017
Country/TerritoryCanada
CityVancouver
Period3/08/174/08/17

Bibliographical note

Publisher Copyright:
© 2017 Association for Computational Linguistics.

Funding

This material is based in part on research sponsored by DARPA under grant number FA8750-13-2-0017 (the DEFT program). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA and the U.S. Government. This work has also been supported by the French National Research Agency under project ANR-16-CE33-0013 and partially supported by an Intel ICRI-CI grant, the Israel Science Foundation grant 880/12, and the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1).

FundersFunder number
DIPDA 1600/1-1
German-Israeli Project Cooperation
Intel ICRI-CI
Defense Advanced Research Projects AgencyFA8750-13-2-0017
Deutsche Forschungsgemeinschaft
Agence Nationale de la RechercheANR-16-CE33-0013
Israel Science Foundation880/12

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