Journal of Computing Science and Engineering

  • 제11권1호
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  • Pages.32-38
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  • 2017
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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Word Sense Disambiguation Using Embedded Word Space

  • Kang, Myung Yun (Department of Business Data Convergence, Chungbuk National University) ;
  • Kim, Bogyum (Department of Computer Science, Chungbuk National University) ;
  • Lee, Jae Sung (Department of Computer Science, Chungbuk National University)
  • 투고 : 2017.01.06
  • 심사 : 2017.03.15
  • 발행 : 2017.03.30
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초록

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.

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참고문헌

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피인용 문헌

  1. Effect of Word Sense Disambiguation on Neural Machine Translation: A Case Study in Korean vol.6, pp.2169-3536, 2018, https://doi.org/10.1109/ACCESS.2018.2851281