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 language | English |
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| Title of host publication | Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014 |
| Publisher | Faculty of Pharmaceutical Sciences, Chulalongkorn University |
| Pages | 135-144 |
| Number of pages | 10 |
| ISBN (Electronic) | 9786165518871 |
| Publication status | Published - 1 Jan 2014 |
| Event | 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014 - Cape Panwa Hotel, Phuket, Thailand Duration: 12 Dec 2014 → 14 Dec 2014 |
Conference
| Conference | 28th Pacific Asia Conference on Language, Information and Computation, PACLIC 2014 |
|---|---|
| Country/Territory | Thailand |
| City | Phuket |
| Period | 12/12/14 → 14/12/14 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science (miscellaneous)