Year: 2014
Type: conference-paper
Abstract: 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.
Institutions: Hong Kong Polytechnic University, University of Pisa
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2251283399