Word Sense Classification Using Support Vector Machines


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 11, pp. 563-568, Nov. 2016
10.3745/KTSDE.2016.5.11.563,   PDF Download:
Keywords: Word Sense Disambiguation, Muliti-Class Classification, Word Embedding, Support Vector Machine
Abstract

The word sense disambiguation problem is to find the correct sense of an ambiguous word having multiple senses in a dictionary in a sentence. We regard this problem as a multi-class classification problem and classify the ambiguous word by using Support Vector Machines. Context words of the ambiguous word, which are extracted from Sejong sense tagged corpus, are represented to two kinds of vector space. One vector space is composed of context words vectors having binary weights. The other vector space has vectors where the context words are mapped by word embedding model. After experiments, we acquired accuracy of 87.0% with context word vectors and 86.0% with word embedding model.


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Cite this article
[IEEE Style]
P. J. Hyeok and L. Songwook, "Word Sense Classification Using Support Vector Machines," KIPS Transactions on Software and Data Engineering, vol. 5, no. 11, pp. 563-568, 2016. DOI: 10.3745/KTSDE.2016.5.11.563.

[ACM Style]
Park Jun Hyeok and Lee Songwook. 2016. Word Sense Classification Using Support Vector Machines. KIPS Transactions on Software and Data Engineering, 5, 11, (2016), 563-568. DOI: 10.3745/KTSDE.2016.5.11.563.