An Intrinsic Information Content Metric for Semantic Similarity in WordNet

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An Intrinsic Information Content Metric for Semantic Similarity in WordNet (2004)

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by Nuno Seco , Tony Veale , Jer Hayes

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119 - 6 self

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@MISC{Seco04anintrinsic,
    author = {Nuno Seco and Tony Veale and Jer Hayes},
    title = {An Intrinsic Information Content Metric for Semantic Similarity in WordNet},
    year = {2004}
}

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Abstract

Information Content (IC) is an important dimension of word knowledge when assessing the similarity of two terms or word senses. The conventional way of measuring the IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with statistics on their actual usage in text as derived from a large corpus. In this paper we present a wholly intrinsic measure of IC that relies on hierarchical structure alone. We report that this measure is consequently easier to calculate, yet when used as the basis of a similarity mechanism it yields judgments that correlate more closely with human assessments than other, extrinsic measures of IC that additionally employ corpus analysis.

Keyphrases

intrinsic information content metric semantic similarity hierarchical structure word sens information content important dimension conventional way word knowledge large corpus actual usage human assessment similarity mechanism extrinsic measure intrinsic measure corpus analysis

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