Browse > Article
http://dx.doi.org/10.3745/KTSDE.2016.5.11.563

Word Sense Classification Using Support Vector Machines  

Park, Jun Hyeok (한국교통대학교 컴퓨터정보공학과)
Lee, Songwook (한국교통대학교 컴퓨터정보공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.5, no.11, 2016 , pp. 563-568 More about this Journal
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.
Keywords
Word Sense Disambiguation; Muliti-Class Classification; Word Embedding; Support Vector Machine;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, "Efficient Estimation of Word Representations in Vector Space," arXiv:1301.3781, 2013.
2 Michael Lesk, "Automatic Sense Disambiguation Using Machine Readable Dictionaries: How to Tell a Pine Cone from an Ice Cream Cone," in Proceedings of the 5th Annual International Conference on Systems Documentation, 1986.
3 Yong-Gu Lee and Young-Mee Chung, "An Experimental Study on an Effective Word Sense Disambiguation Model Based on Automatic Sense Tagging Using Dictionary Information," Journal of the Korean Society for Information Management, Vol.24, No.1, pp.321-342, 2007.
4 Jung-Gil Cho and Kwang-Cheul Shin, "A Graph-based Word Sense Disambiguation Using Measures of Graph Connectivity," Journal of Korean Institute of Information Technology, Vol.12, No.6, pp.143-151, 2014.
5 Dongsuk O, Sangwoo Kang, and Jungyun Seo, "An Iterative Approach to Graph-based Word Sense Disambiguation Using Word2ec," Korean Journal of Cognitive Science, Vol.2, No.1, pp.43-60, 2016.
6 Yong Min Park and Jae Sung Lee, "Word Sense Disambiguation using Korean Word Space Model," Journal of the Korea Contents Association, Vol.12, No.6, pp.41-47, 2012.   DOI
7 Myung Yun Kang, Bogyum Kim, and Jae Sung Lee, "Word Sense Disambiguation using Word2Vec," in Proceedings of the 27th Annual Conference on Human & Cognitive Language Technology, pp.81-84, 2015.
8 Sangwook Kang, Minho Kim, Hyuk-chul Kwon, and Jyhyun Oh, "Word Sense Disambiguation of Predicate using Semi-supervised Learning and Sejong Electronic Dictionary," KIISE Transactions on Computing Practices, Vol.22, No.2, pp.107-112, 2016.   DOI
9 Scikit Learn, 2016 [Internet], http://scikit-learn.org/stable/modules/svm.html.
10 Yeohoon Yoon, "Word sense disambiguation through the acyclic semantic transition network," Master thesis, Sogang University, 2003.
11 SangKeun Park, Jeeyeon Choi, and Key-Sun Choi, "Word Sense Disambiguation using Dynamic Sized Window and Frequency Weighting," Korea Information Science Society, pp.441-443, 2014.