Unsupervised Semantic Similarity Computation using Web Search Engines
Iosif, Elias
Potamianos, Alexandros
This paper appears in: Web Intelligence, IEEE/WIC/ACM International Conference on
Publication Date: 2-5 Nov. 2007
On page(s): 381-387
Location: Fremont, CA,
ISBN: 978-0-7695-3026-0
Digital Object Identifier: 10.1109/WI.2007.34
Current Version Published: 2008-01-07
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 "widecontext" 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.
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