Knowing the degree of antonymy between words has widespread applications in natural language processing. Manually-created lexicons have limited coverage and do not include most semantically contrasting word pairs. We present a new automatic and empirical measure of antonymy that combines corpus statistics with the structure of a published thesaurus. The approach is evaluated on a set of closest-opposite questions, obtaining a precision of over 80%. Along the way, we discuss what humans consider antonymous and how antonymy manifests itself in utterances. 1

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Paraphrases are useful for statistical machine translation (SMT) and natural language processing tasks. Distributional paraphrase generation is independent of parallel texts and syntactic parses, and hence is suitable also for resource-poor languages, but tends to erroneously rank antonyms, trend-contrasting, and polarity-dissimilar candidates as good paraphrases. We present here a novel method for improving distributional paraphrasing by filtering out such candidates. We evaluate it in simulated low and mid-resourced SMT tasks, translating from English to two quite different languages. We show statistically significant gains in English-to-Chinese translation quality, up to 1 BLEU from nonfiltered paraphrase-augmented models (1.6 BLEU from baseline). We also show that yielding gains in translation to Arabic, a morphologically rich language, is not straightforward. 1