@misc{cogprints6181, month = {August}, title = {A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations}, author = {Peter D. Turney}, year = {2008}, pages = {905--912}, note = {NRC 50398}, keywords = {analogies, synonyms, antonyms, associations, distributional hypothesis, semantics}, url = {http://cogprints.org/6181/}, abstract = {Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.} }