Using Three Way Data for Word Sense Discrimination

In this paper, an extension of a dimensionality reduction algorithm called NONNEGATIVE MATRIX FACTORIZATION is presented that combines both `bag of words' data and syntactic data, in order to find 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 semantic dimensions found by the bag of words approach. The novel approach is embedded into clustering algorithms, to make it fully automatic. The approach is carried out for Dutch, and evaluated against EuroWordNet.

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Tim Van de Cruys
• Keywords: COLING 2008, Computational Linguistics, Dimensionality Reduction Algorithm, Nonnegative Matrix Factorization, Semantic Dimensions