This site has been permanently archived. This is a static copy provided by the University of Southampton.

%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