An English-Chinese cross-lingual word semantic similarity measure exploring attributes and relations

Lin Dai*, Heyan Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Word semantic similarity measuring is a fundamental issue to many NLP applications and the globalization has made an urgent request for cross-lingual word similarity measure. This paper proposed a word semantic similarity measure which is able to work in cross-lingual scenarios. Basically, a concept can be defined by a set of attributes. The basic idea of this work is to compute the similarity between words by exploring their attributes and relations. For a given word pair, we first compute similarities between their attributes by combining distance, depth and relation information. Then word similarity are computed through a combination scheme. The algorithm is implemented based on an English-Chinese bilingual ontology HowNet. Experiments show that the proposed algorithm results in high correlation against human judgments, which encourages its broad application in cross-lingual applications.

Original languageEnglish
Title of host publicationPACLIC 25 - Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation
Pages467-476
Number of pages10
Publication statusPublished - 2011
Event25th Pacific Asia Conference on Language, Information and Computation, PACLIC 25 - , Singapore
Duration: 16 Dec 201118 Dec 2011

Publication series

NamePACLIC 25 - Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

Conference

Conference25th Pacific Asia Conference on Language, Information and Computation, PACLIC 25
Country/TerritorySingapore
Period16/12/1118/12/11

Keywords

  • Computing linguistics
  • Cross-lingual
  • Natural language processing
  • Word semantic similarity

Fingerprint

Dive into the research topics of 'An English-Chinese cross-lingual word semantic similarity measure exploring attributes and relations'. Together they form a unique fingerprint.

Cite this