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bibo:abstract "Recognizing analogies, synonyms, antonyms, and associations appear to be four\r\ndistinct tasks, requiring distinct NLP algorithms. In the past, the four\r\ntasks have been treated independently, using a wide variety of algorithms.\r\nThese four semantic classes, however, are a tiny sample of the full\r\nrange of semantic phenomena, and we cannot afford to create ad hoc algorithms\r\nfor each semantic phenomenon; we need to seek a unified approach.\r\nWe propose to subsume a broad range of phenomena under analogies.\r\nTo limit the scope of this paper, we restrict our attention to the subsumption\r\nof synonyms, antonyms, and associations. We introduce a supervised corpus-based\r\nmachine learning algorithm for classifying analogous word pairs, and we\r\nshow that it can solve multiple-choice SAT analogy questions, TOEFL\r\nsynonym questions, ESL synonym-antonym questions, and similar-associated-both\r\nquestions from cognitive psychology."^^xsd:string;
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