Mining the Web for Synonyms: PMI-IR Versus LSA on TOEFL

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TypeArticle
ConferenceProceedings of the Twelfth European Conference on Machine Learning (ECML-2001), September 3-7, 2001., Freiburg, Germany
AbstractThis 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).
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number44893
NPARC number5765594
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Record identifierca6e99f8-38bc-4445-8e0d-f6a315391541
Record created2009-03-29
Record modified2016-05-09
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