  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.
