A supervised classification approach for measuring relational similarity between word pairs

Danushka Bollegala*, Yutaka Matsuo, Mitsuru Ishizuka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Measuring the relational similarity between word pairs is important in numerous natural language processing tasks such as solving word analogy questions, classifying noun-modifier relations and disambiguating word senses. We propose a supervised classification method to measure the similarity between semantic relations that exist between words in two word pairs. First, each pair of words is represented by a vector of automatically extracted lexical patterns. Then a binary Support Vector Machine is trained to recognize word pairs with similar semantic relations to a given word pair. To train and evaluate the proposed method, we use a benchmark dataset that contains 374 SAT multiple-choice word-analogy questions. To represent the relations that exist between two word pairs, we experiment with 11 different feature functions, including both symmetric and asymmetric feature functions. Our experimental results show that the proposed method outperforms several previously proposed relational similarity measures on this benchmark dataset, achieving an SAT score of 46.9.

Original languageEnglish
Pages (from-to)2227-2233
Number of pages7
JournalIEICE Transactions on Information and Systems
Issue number11
Publication statusPublished - 2011 Nov
Externally publishedYes


  • Relational similarity
  • Supervised classification
  • Support vector machines
  • Word analogies

ASJC Scopus subject areas

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


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