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--- 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). \n\n" altloc: - http://extractor.iit.nrc.ca/reports/ecml2001.html chapter: ~ commentary: ~ commref: ~ confdates: 'September 3-7, 2001' conference: Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) confloc: 'Freiburg, Germany' contact_email: ~ creators_id: [] creators_name: - family: Turney given: Peter honourific: '' lineage: '' date: 2001 date_type: published datestamp: 2001-09-12 department: ~ dir: disk0/00/00/17/96 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: - family: De Raedt given: Luc honourific: '' lineage: '' - family: Flach given: Peter honourific: '' lineage: '' eprint_status: archive eprintid: 1796 fileinfo: /style/images/fileicons/application_postscript.png;/1796/1/ECML2001.ps|/style/images/fileicons/application_pdf.png;/1796/5/ECML2001.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'PMI-IR, synonyms, LSA, LSI, Latent Semantic Analysis, text mining, web mining, TOEFL, mutual information' lastmod: 2011-03-11 08:54:47 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 491-502 pubdom: FALSE publication: ~ publisher: Springer-Verlag refereed: TRUE referencetext: |- 1. 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Harman (ed.), The Third Text REtrieval Conference (TREC3), National Institute of Standards and Technology Special Publication 500-226, Gaithersburg, Maryland (1994) C1-C4. 27. Buckley, C., Salton, G., Allan, J., Singhal, A.: Automatic Query Expansion Using SMART: TREC 3. In: The Third Text REtrieval Conference (TREC3), D. Harman (ed.), National In-stitute of Standards and Technology Special Publication 500-226, Gaithersburg, Maryland (1994) 69-80. relation_type: [] relation_uri: [] reportno: ~ rev_number: 14 series: ~ source: ~ status_changed: 2007-09-12 16:40:36 subjects: - comp-sci-lang - comp-sci-mach-learn - comp-sci-stat-model succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL' type: confpaper userid: 2175 volume: ~