COMPARABLE EVALUATION OF CONTEMPORARY CORPUS-BASED AND KNOWLEDGE-BASED SEMANTIC SIMILARITY MEASURES OF SHORT TEXTS
Journal Title: Journal of Information Technology and Application (JITA) - Year 2011, Vol 1, Issue 1
Abstract
This paper presents methods for measuring the semantic similarity of texts, where we evaluated different approaches based on existing similarity measures. On one side word similarity was calculated by processing large text corpuses and on the other, commonsense knowledgebase was used. Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientifi c documents, image captions or product descriptions), where commonsense knowledge has an important role, in this paper we focus on computing the similarity between two sentences or two short paragraphs by extending existing measures with information from the ConceptNet knowledgebase. On the other hand, an extensive research has been done in the fi eld of corpus-based semantic similarity, so we also evaluated existing solutions by imposing some modifi cations. Through experiments performed on a paraphrase data set, we demonstrate that some of proposed approaches can improve the semantic similarity measurement of short text.
Authors and Affiliations
Bojan Furlan, Vladimir Sivački, Davor Jovanović, Boško Nikolić
Keywords
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- EP ID EP244668
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