Abstract
Although semantic distance measures are applied to words in textual tasks such as building lexical chains, semantic distance is really a property of concepts, not words. After discussing the limitations of measures based solely on lexical resources such as WordNet or solely on distributional data from text corpora, we present a hybrid measure of semantic distance based on distributional profiles of concepts that we infer from corpora. We use only a very coarse-grained inventory of concepts—each category of a published thesaurus is taken as a single concept—and yet we obtain results on basic semantic-distance tasks that are better than those of methods that use only distributional data and are generally as good as those that use fine-grained WordNet-based measures. Because the measure is based on naturally occurring text, it is able to find word pairs that stand in non-classical relationships not found in WordNet. It can be applied cross-lingually, using a thesaurus in one language to measure semantic distance between words in another. In addition, we show the use of the method in determining the degree of antonymy of word pairs.
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Hirst, G., Mohammad, S. (2011). Semantic Distance Measures with Distributional Profiles of Coarse-Grained Concepts. In: Mehler, A., Kühnberger, KU., Lobin, H., Lüngen, H., Storrer, A., Witt, A. (eds) Modeling, Learning, and Processing of Text Technological Data Structures. Studies in Computational Intelligence, vol 370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22613-7_4
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DOI: https://doi.org/10.1007/978-3-642-22613-7_4
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