Unsupervised semantic similarity computation using web search engines

Elias Iosif, Alexandras Potamianos

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

Abstract

In this paper, we propose two novel web-based metrics for semantic similarity computation between words. Both metrics use a web search engine in order to exploit the retrieved information for the words of interest. The first metric considers only the page counts returned by a search engine, based on the work of [1]. The second downloads a number of the top ranked documents and applies "wide-context" and "narrow-context" metrics. The proposed metrics work automatically, without consulting any human annotated knowledge resource. The metrics are compared with WordNet-based methods. The metrics' performance is evaluated in terms of correlation with respect to the pairs of the commonly used Charles - Miller dataset. The proposed "wide-context" metric achieves 71% correlation, which is the highest score achieved among the fully unsupervised metrics in the literature up to date.

Original languageEnglish
Title of host publicationProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Pages381-387
Number of pages7
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 - Silicon Valley, CA, United States
Duration: 2 Nov 20075 Nov 2007

Publication series

NameProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007

Conference

ConferenceIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Country/TerritoryUnited States
CitySilicon Valley, CA
Period2/11/075/11/07

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