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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 12 of 12 1 A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations Turney, Peter D.. - 2008 BASE Show details 2 The Latent Relation Mapping Engine: Algorithm and Experiments [<Journal>] Turney, Peter D.. - : AI Access Foundation BASE Show details 3 Empirical Evaluation of Four Tensor Decomposition Algorithms Turney, Peter D.. - 2007 BASE Show details 4 Expressing Implicit Semantic Relations without Supervision Turney, Peter D.. - 2006 BASE Show details 5 Similarity of Semantic Relations [<Journal>] Turney, Peter D. BASE Show details 6 Corpus-based Learning of Analogies and Semantic Relations [<Journal>] Littman, Michael L.; Turney, Peter D.. - : Springer BASE Show details 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/ BASE Hide details 8 Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities Turney, Peter D.. - 2004 BASE Show details 9 Combining Independent Modules in Lexical Multiple-Choice Problems Turney, Peter D.; Littman, Michael L.; Bigham, Jeffrey. - : John Benjamins, 2004 BASE Show details 10 Human-Level Performance on Word Analogy Questions by Latent Relational Analysis Turney, Peter D.. - 2004 BASE Show details 11 Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus Turney, Peter D.; Littman, Michael L.. - 2002 BASE Show details 12 Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews Turney, Peter D.. - 2002 BASE Show details

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