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1
A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations
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.
Keyword: Artificial Intelligence; Computational Linguistics; Language; Machine Learning; Semantics
URL: http://cogprints.org/6181/
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2
The Latent Relation Mapping Engine: Algorithm and Experiments [<Journal>]
Turney, Peter D.. - : AI Access Foundation
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3
Empirical Evaluation of Four Tensor Decomposition Algorithms
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4
Expressing Implicit Semantic Relations without Supervision
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5
Similarity of Semantic Relations [<Journal>]
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6
Corpus-based Learning of Analogies and Semantic Relations [<Journal>]
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7
Measuring Semantic Similarity by Latent Relational Analysis
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8
Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities
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9
Combining Independent Modules in Lexical Multiple-Choice Problems
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10
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
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11
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
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12
Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews
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