Abstract
AbstractA corpus-based Measure of Semantic Relatedness can be calculated for every pair of words occurring in the corpus, but it can produce erroneous results for many word pairs due to accidental associations derived on the basis of several context features. We propose a novel idea of a partial measure that assigns relatedness values only to word pairs well enough supported by corpus data. Three simple implementations of this idea are presented and evaluated on large corpora and wordnets for two languages. Partial Measures of Semantic Relatedness are shown to perform better in tasks focused on wordnet development than a state-of-the-art ‘full’ Measure of Semantic Relatedness. A comparison of the partial measure with a globally filtered measure is also presented.KeywordsWord PairSemantic RelatednessComputational LinguisticsPartial MeasureSemantic ClassisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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