Exploring Three Way Contexts for Word Sense Discrimination
Tim Van de Cruys, M. Baroni, A. Lenci, M. Sahlgren · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2007
In this paper, an extension of a dimensionality reduction algorithm called non-negative matrix factorization is presented that combines ‘bag of words’ data and syntactic data, in order to find latent semantic dimensions according to which both words and syntactic relations can be classified. The use of three way data allows one to determine which dimension(s) are responsible for a certain sense of a word, and adapt the corresponding feature vector accordingly, ‘subtracting’ one sense to discover another one. The intuition in this is that the syntactic features of the syntax-based approach can be disambiguated by the latent semantic dimensions found by the bag of words approach.