Paper: Latent Vector Weighting for Word Meaning in Context

ACL ID D11-1094
Title Latent Vector Weighting for Word Meaning in Context
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

This paper presents a novel method for the com- putation of word meaning in context. We make use of a factorization model in which words, to- gether with their window-based context words and their dependency relations, are linked to latent dimensions. The factorization model al- lows us to determine which dimensions are im- portant for a particular context, and adapt the dependency-based feature vector of the word accordingly. The evaluation on a lexical substi- tution task – carried out for both English and French – indicates that our approach is able to reach better results than state-of-the-art meth- ods in lexical substitution, while at the same time providing more accurate meaning repre- sentations.