Efficient Ranking and Computation of Semantic Relatedness and its Application to Word Sense Disambiguation.
Efficient Ranking and Computation of Semantic Relatedness and its Application to Word Sense Disambiguation.
Authors
Abstract
Wikipedia has grown in to a high quality up-to-date knowledge base and can enable many intelligent systems that rely on semantic information. One of the most general and quite powerful semantic tools is a measure of semantic relatedness between concepts. Moreover, the ability to efficiently produce a list of ranked similar concepts for a given concept is very important for a wide range of applications. We propose to use a simple measure of similarity between Wikipedia concepts, based on Dice's measure, and provide very efficient heuristic methods to compute top k ranking results. We also present a randomised algorithm that speeds up the evaluation of the measure for a pair of articles. Furthermore, since our heuristics are based on statistical properties of scale-free networks, we show that these heuristics are applicable to other complex ontologies. Finally, in order to evaluate the measure, we have used it to solve the problem of word-sense disambiguation. Our approach to word sense disambiguation is based solely on the similarity measure and produces results with high accuracy.
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Technical Report, 2008.
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All publications during 2008
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