A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web

Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

研究成果: Conference contribution

39 被引用数 (Scopus)

抄録

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychology. Accurate measurement of semantic similarity between words is essential for various tasks such as, document clustering, information retrieval, and synonym extraction. We propose a novel model of semantic similarity using the semantic relations that exist among words. Given two words, first, we represent the semantic relations that hold between those words using automatically extracted lexical pattern clusters. Next, the semantic similarity between the two words is computed using a Mahalanobis distance measure. We compare the proposed similarity measure against previously proposed semantic similarity measures on Miller-Charles benchmark dataset and WordSimilarity-353 collection. The proposed method outperforms all existing web-based semantic similarity measures, achieving a Pearson correlation coefficient of 0.867 on the Millet-Charles dataset.

本文言語English
ホスト出版物のタイトルEMNLP 2009 - Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: A Meeting of SIGDAT, a Special Interest Group of ACL, Held in Conjunction with ACL-IJCNLP 2009
ページ803-812
ページ数10
出版ステータスPublished - 2009
外部発表はい
イベント2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore
継続期間: 2009 8 62009 8 7

Other

Other2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
CitySingapore
Period09/8/609/8/7

ASJC Scopus subject areas

  • 計算理論と計算数学
  • コンピュータ サイエンスの応用
  • 情報システム

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