%A Peter Turney
%T Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
%X 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). 


%K PMI-IR, synonyms, LSA, LSI, Latent Semantic Analysis, text mining, web mining, TOEFL, mutual information
%P 491-502
%E Luc De Raedt
%E Peter Flach
%D 2001
%I Springer-Verlag
%L cogprints1796

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