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

Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2116-2123
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE95-D
Issue number8
DOIs
Publication statusPublished - 2012 Aug
Externally publishedYes

Keywords

  • Attributional similarity
  • Miller-Charles dataset
  • Relational similarity
  • Synonymy
  • WordSimilarity-353 dataset

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Software
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition

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