Improving relational similarity measurement using symmetries in proportional word analogies

Danushka Bollegala, Tomokazu Goto, Nguyen Tuan Duc, Mitsuru Ishizuka

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Measuring the similarity between the semantic relations that exist between words is an important step in numerous tasks in natural language processing such as answering word analogy questions, classifying compound nouns, and word sense disambiguation. Given two word pairs (A, B) and (C, D), we propose a method to measure the relational similarity between the semantic relations that exist between the two words in each word pair. Typically, a high degree of relational similarity can be observed between proportional analogies (i.e. analogies that exist among the four words, A is to B such as C is to D). We describe eight different types of relational symmetries that are frequently observed in proportional analogies and use those symmetries to robustly and accurately estimate the relational similarity between two given word pairs. We use automatically extracted lexical-syntactic patterns to represent the semantic relations that exist between two words and then match those patterns in Web search engine snippets to find candidate words that form proportional analogies with the original word pair. We define eight types of relational symmetries for proportional analogies and use those as features in a supervised learning approach. We evaluate the proposed method using the Scholastic Aptitude Test (SAT) word analogy benchmark dataset. Our experimental results show that the proposed method can accurately measure relational similarity between word pairs by exploiting the symmetries that exist in proportional analogies. The proposed method achieves an SAT score of 49.2% on the benchmark dataset, which is comparable to the best results reported on this dataset.

Original languageEnglish
Pages (from-to)355-369
Number of pages15
JournalInformation Processing and Management
Volume49
Issue number1
DOIs
Publication statusPublished - 2013 Jan
Externally publishedYes

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Semantics
scholastic aptitude
aptitude test
semantics
Supervised learning
Syntactics
Search engines
search engine
candidacy
Processing
Symmetry
language
learning

Keywords

  • Proportional analogy
  • Relational similarity
  • SAT dataset

ASJC Scopus subject areas

  • Media Technology
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences
  • Management Science and Operations Research

Cite this

Improving relational similarity measurement using symmetries in proportional word analogies. / Bollegala, Danushka; Goto, Tomokazu; Duc, Nguyen Tuan; Ishizuka, Mitsuru.

In: Information Processing and Management, Vol. 49, No. 1, 01.2013, p. 355-369.

Research output: Contribution to journalArticle

Bollegala, Danushka ; Goto, Tomokazu ; Duc, Nguyen Tuan ; Ishizuka, Mitsuru. / Improving relational similarity measurement using symmetries in proportional word analogies. In: Information Processing and Management. 2013 ; Vol. 49, No. 1. pp. 355-369.
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