Learning Scalar Adjective Intensity from Paraphrases

Abstract

Adjectives like ``warm'', ``hot'', and ``scalding'' all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair ``really hot'' -- ``scalding'' suggests that ``hot'' ``scalding''. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to ``yes/no'' questions.

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Reproductions