Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy


IEICE TRANSACTIONS on Information and Systems   Vol.E95-D    No.8    pp.2116-2123
Publication Date: 2012/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.2116
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Natural Language Processing
synonymy,  attributional similarity,  relational similarity,  Miller-Charles dataset,  WordSimilarity-353 dataset,  

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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 existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.

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