title: A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations
creator: Turney, Peter D.
subject: Language
subject: Computational Linguistics
subject: Semantics
subject: Machine Learning
subject: Artificial Intelligence
description: 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.
date: 2008-08
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/6181/1/turney_coling08.pdf
identifier:   Turney, Peter D.  (2008) A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations.  [Conference Paper]     
relation: http://cogprints.org/6181/