2001 MiningTheWebForSynonyms

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Subject Headings: Pointwise Mutual Information and Information Retrieval, Synonym Extraction Algorithm, Lexical Semantic Similarity Function.

Notes

Cited By

2002

Quotes

Abstract

This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirically evaluated using 80 synonym test questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym test questions from a collection of tests for students of English as a Second Language (ESL). On both tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL questions. The paper discusses potential applications of the new unsupervised learning algorithm and some implications of the results for LSA and LSI (Latent Semantic Indexing).


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2001 MiningTheWebForSynonymsPeter D. TurneyMining the Web for Synonyms: PMI-IR versus LSA on TOEFLECML 2001http://arxiv.org/ftp/cs/papers/0212/0212033.pdf10.1007/3-540-44795-4_422001
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