Latent vector weighting for word meaning in context

Authors:
Tim Van de Cruys;Thierry Poibeau;Anna Korhonen
Affiliations:
RCEAL, University of Cambridge;LaTTiCe, CNRS & ENS;University of Cambridge
Venue:
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Year:
2011

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Abstract

This paper presents a novel method for the computation of word meaning in context. We make use of a factorization model in which words, together with their window-based context words and their dependency relations, are linked to latent dimensions. The factorization model allows us to determine which dimensions are important for a particular context, and adapt the dependency-based feature vector of the word accordingly. The evaluation on a lexical substitution task -- carried out for both English and French -- indicates that our approach is able to reach better results than state-of-the-art methods in lexical substitution, while at the same time providing more accurate meaning representations.