Measuring the degree of synonymy between words using relational similarity between word pairs as a proxy

Danushka Bollegala*, Yutaka Matsuo, Mitsuru Ishizuka

*この研究の対応する著者

研究成果査読

2 被引用数 (Scopus)

抄録

Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existingWeb-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.

本文言語English
ページ(範囲)2116-2123
ページ数8
ジャーナルIEICE Transactions on Information and Systems
E95-D
8
DOI
出版ステータスPublished - 2012 8
外部発表はい

ASJC Scopus subject areas

  • 電子工学および電気工学
  • ソフトウェア
  • 人工知能
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識

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