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

Turney, Peter (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. [Conference Paper]

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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).

Item Type:Conference Paper
Keywords:PMI-IR, synonyms, LSA, LSI, Latent Semantic Analysis, text mining, web mining, TOEFL, mutual information
Subjects:Computer Science > Language
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1796
Deposited By: Turney, Peter
Deposited On:12 Sep 2001
Last Modified:11 Mar 2011 08:54

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