JCSE, vol. 11, no. 1, pp.32-38, March, 2017

DOI: http://dx.doi.org/10.5626/JCSE.2017.11.1.32

Word Sense Disambiguation Using Embedded Word Space

Myung Yun Kang, Bogyum Kim and Jae Sung Lee
Department of Business Data Convergence, Chungbuk National University, Cheongju, Korea Department of Computer Science, Chungbuk National University, Cheongju, Korea

Abstract: Determining the correct word sense among ambiguous senses is essential for semantic analysis. One of the models for word sense disambiguation is the word space model which is very simple in the structure and effective. However, when the context word vectors in the word space model are merged into sense vectors in a sense inventory, they become typically very large but still suffer from the lexical scarcity. In this paper, we propose a word sense disambiguation method using word embedding that makes the sense inventory vectors compact and efficient due to its additive compositionality. Results of experiments with a Korean sense-tagged corpus show that our method is very effective.

Keyword: Word sense disambiguation; Word embedding; Word space; Semantic analysis

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