4.7 Article

Word sense disambiguation based on context selection using knowledge-based word similarity

期刊

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2021.102551

关键词

Natural language processing; Word sense disambiguation; Knowledge-based word vector representation; Similarity-based word selection

资金

  1. Institute for Information communications Technology Planning Evaluation (IITP) - Korea government (MSIT) [2020-0-00368]
  2. National Research Foundation of Korea (NRF) - Korea government (MSIT) [2020R1A2C2100362]
  3. National Research Foundation of Korea [2020R1A2C2100362] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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A novel knowledge-based word-sense disambiguation system is introduced in this study, which significantly enhances the performance by using word vector representation and word similarity analysis.
In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships from the lexical knowledge bases, and the word vector representation is utilized to determine the word similarity in our WSD system. Second, we present an effective method for extracting the contextual words from a text for analyzing an ambiguous word based on word similarity. The results demonstrate that the suggested methods significantly enhance the baseline WSD performance in all corpora. In particular, the performance on nouns is similar to those of the state-of-the-art knowledge-based WSD models, and the performance on verbs surpasses that of the existing knowledge-based WSD models.

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