TY - GEN ID - cogprints1796 UR - http://cogprints.org/1796/ A1 - Turney, Peter Y1 - 2001/// N2 - 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). PB - Springer-Verlag KW - PMI-IR KW - synonyms KW - LSA KW - LSI KW - Latent Semantic Analysis KW - text mining KW - web mining KW - TOEFL KW - mutual information TI - Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL SP - 491 AV - public EP - 502 ER -