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月 6 → 2009 8月 7 |
Other
Other | 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 |
---|---|
City | Singapore |
Period | 09/8/6 → 09/8/7 |
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