title: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
creator: Turney, Peter
subject: Language
subject: Machine Learning
subject: Statistical Models
description: 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).   
publisher: Springer-Verlag
contributor: De Raedt, Luc
contributor: Flach, Peter
date: 2001
type: Conference Paper
type: PeerReviewed
format: application/postscript
identifier: http://cogprints.org/1796/1/ECML2001.ps
format: application/pdf
identifier: http://cogprints.org/1796/5/ECML2001.pdf
identifier:   Turney, Peter  (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL.  [Conference Paper]     
relation: http://cogprints.org/1796/