Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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Semantic Word Categorization using Feature Similarity based K Nearest Neighbor

  • Jo, Taeho (School of Game, Hongik University)
  • Received : 2018.02.27
  • Accepted : 2018.05.14
  • Published : 2018.06.30
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Abstract

This article proposes the modified KNN (K Nearest Neighbor) algorithm which considers the feature similarity and is applied to the word categorization. The texts which are given as features for encoding words into numerical vectors are semantic related entities, rather than independent ones, and the synergy effect between the word categorization and the text categorization is expected by combining both of them with each other. In this research, we define the similarity metric between two vectors, including the feature similarity, modify the KNN algorithm by replacing the exiting similarity metric by the proposed one, and apply it to the word categorization. The proposed KNN is empirically validated as the better approach in categorizing words in news articles and opinions. The significance of this research is to improve the classification performance by utilizing the feature similarities.

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References

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