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We describe the use of a weakly supervised bootstrapping algorithm in discovering contrasting semantic categories from a source lexicon with little training data. Our method primarily exploits the patterns in sentential contexts where different categories of words may appear. Experimental results are presented showing that such automatically categorized terms tend to agree with human judgements.

Building a semantic lexicon of English nouns via bootstrapping