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

  • Peter D. Turney
Conference paper

DOI: 10.1007/3-540-44795-4_42

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2167)
Cite this paper as:
Turney P.D. (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In: De Raedt L., Flach P. (eds) Machine Learning: ECML 2001. ECML 2001. Lecture Notes in Computer Science, vol 2167. Springer, Berlin, Heidelberg

Abstract

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).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Peter D. Turney
    • 1
  1. 1.Institute for Information TechnologyNational Research Council of CanadaOttawaCanada

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