Taking antonymy mask off in vector space

Enrico Santus, Qin Lu, Alessandro Lenci, Chu-ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

18 Citations (Scopus)

Abstract

2014 by Enrico Santus, Qin Lu, Alessandro Lenci and Chu-Ren Huang. Automatic detection of antonymy is an important task in Natural Language Processing (NLP) for Information Retrieval (IR), Ontology Learning (OL) and many other semantic applications. However, current unsupervised approaches to antonymy detection are still not fully effective because they cannot discriminate antonyms from synonyms. In this paper, we introduce APAnt, a new Average-Precision-based measure for the unsupervised discrimination of antonymy from synonymy using Distributional Semantic Models (DSMs). APAnt makes use of Average Precision to estimate the extent and salience of the intersection among the most descriptive contexts of two target words. Evaluation shows that the proposed method is able to distinguish antonyms and synonyms with high accuracy across different parts of speech, including nouns, adjectives and verbs. APAnt outperforms the vector cosine and a baseline model implementing the cooccurrence hypothesis.
Original languageEnglish
Title of host publicationProceedings of the 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014
PublisherFaculty of Pharmaceutical Sciences, Chulalongkorn University
Pages135-144
Number of pages10
ISBN (Electronic)9786165518871
Publication statusPublished - 1 Jan 2014
Event28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014 - Cape Panwa Hotel, Phuket, Thailand
Duration: 12 Dec 201414 Dec 2014

Conference

Conference28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014
CountryThailand
CityPhuket
Period12/12/1414/12/14

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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