Building a Lexical Knowledge-Base of Near-Synonym Differences
Diana Inkpen
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Building a Lexical Knowledge-Base of Near-Synonym Differences

Automatic Knowledge Acquisition

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Current natural language generation or machine translation systems cannot distinguish among near-synonyms - words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between near-synonyms in existing computational lexical resources. In this work, I automatically acquired a lexical knowledge-base of near-synonym differences from multiple sources, using an unsupervised decision- list algorithm. The main types of differences are: stylistic (for example, "inebriated" is more formal than "drunk"), attitudinal (for example, "skinny...