MODIFIED WORD SENSE DISAMBIGUATION METHOD BASED ON DISTRIBUTED REPRESENTATION METHODS

Abstract

In the text mining tasks, textual representation should be not only efficient but also interpretable, as this enables an understanding of the operational logic underlying the data mining models. This paper describes a modified Word Sense Disambiguation (WSD) method which extends two well-known variations of the Lesk WSD approach. Given a word and its context, Lesk bases its calculations on the overlap between the context of a word and each definition of its senses (gloss) in order to select the proper meaning. The main contribution of the proposed method is the adoption of the concept of “similarity” between definition and context instead of "overlap", in addition to expanding the definition with examples provided by WordNet for each sense of the target word. The proposed method is also characterized by the use of text similarity measurement functions defined in a distributed semantic space. The proposed method has been tested on five different benchmark datasets for words sense disambiguation tasks and compared with several basic methods, including simple Lesk, extended Lesk, WordNet 1st sense, Babelfy and UKB. The results show that proposed method outperforms most basic methods with the exception of Babelfy and the WN 1st sense methods.