Combination Features for Semantic Similarity Measure

Dat HUYNH, Dat TRAN, Wanli MA

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

2 Citations (Scopus)


Computing the semantic similarity between words is one of the key tasks in many language-based applications. Recent work has focused on using contextual clues for semantic similarity computation. In this paper, we propose a method to the measure semantic similarity between words using plain text contents. It takes into account information attributes (local) and topic information (global) of words to disclose their semantic similarity scores. The method models the representation of a word as a high dimensional vector of word attributes and latent topics. Thus, the semantic similarity between two words is measured by the semantic distance between their respective vectors. We have tested the proposed method on WordSimilarity-353 dataset. The empirical results have shown the combination features contribute to improve the semantic similarity results the dataset in comparison with previous work on the same task using plain text contents.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2014
EditorsS.I Ao, Oscar Castillo, Craig Douglas, David Dagan Feng, Jeong-A Lee
Place of PublicationHong Kong
PublisherNewswood Limited
Number of pages4
ISBN (Print)9783319116983
Publication statusPublished - 2014
EventInternational Multi Conference of Engineers and Computer Scientists 2014 - Hong Kong, Hong Kong, China
Duration: 12 Mar 201414 Mar 2014

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958


ConferenceInternational Multi Conference of Engineers and Computer Scientists 2014
Abbreviated titleIMECS 2014
CityHong Kong


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