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Turney, Peter D. (12)
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Hits 1 – 12 of 12
1
A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations
Turney, Peter D.
. - 2008
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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
Turney, Peter D.
. - 2007
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4
Expressing Implicit Semantic Relations without Supervision
Turney, Peter D.
. - 2006
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5
Similarity of Semantic Relations [<Journal>]
Turney, Peter D.
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6
Corpus-based Learning of Analogies and Semantic Relations [<Journal>]
Littman, Michael L.
;
Turney, Peter D.
. - : Springer
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7
Measuring Semantic Similarity by Latent Relational Analysis
Turney, Peter D.
. - 2005
Abstract:
This paper introduces Latent Relational Analysis (LRA), a method for measuring semantic similarity. LRA measures similarity in the semantic relations between two pairs of words. When two pairs have a high degree of relational similarity, they are analogous. For example, the pair cat:meow is analogous to the pair dog:bark. There is evidence from cognitive science that relational similarity is fundamental to many cognitive and linguistic tasks (e.g., analogical reasoning). In the Vector Space Model (VSM) approach to measuring relational similarity, the similarity between two pairs is calculated by the cosine of the angle between the vectors that represent the two pairs. The elements in the vectors are based on the frequencies of manually constructed patterns in a large corpus. LRA extends the VSM approach in three ways: (1) patterns are derived automatically from the corpus, (2) Singular Value Decomposition is used to smooth the frequency data, and (3) synonyms are used to reformulate word pairs. This paper describes the LRA algorithm and experimentally compares LRA to VSM on two tasks, answering college-level multiple-choice word analogy questions and classifying semantic relations in noun-modifier expressions. LRA achieves state-of-the-art results, reaching human-level performance on the analogy questions and significantly exceeding VSM performance on both tasks.
Keyword:
Artificial Intelligence
;
Computational Linguistics
;
Language
;
Machine Learning
;
Semantics
URL:
http://cogprints.org/4501/
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8
Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities
Turney, Peter D.
. - 2004
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9
Combining Independent Modules in Lexical Multiple-Choice Problems
Turney, Peter D.
;
Littman, Michael L.
;
Bigham, Jeffrey
. - : John Benjamins, 2004
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10
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
Turney, Peter D.
. - 2004
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11
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus
Turney, Peter D.
;
Littman, Michael L.
. - 2002
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12
Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews
Turney, Peter D.
. - 2002
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