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--- abstract: |- This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes. altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: '8-12 May, 2005' conference: 18th Conference of the Canadian Society for Computational Studies of Intelligence confloc: 'Victoria, BC, Canada' contact_email: ~ creators_id: [] creators_name: - family: Nadeau given: David honourific: '' lineage: '' - family: Turney given: Peter honourific: '' lineage: '' date: 2005 date_type: published datestamp: 2005-06-19 department: ~ dir: disk0/00/00/43/99 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: - family: K�gl given: Bal�zs honourific: '' lineage: '' - family: Lapalme given: Guy honourific: '' lineage: '' eprint_status: archive eprintid: 4399 fileinfo: /style/images/fileicons/application_pdf.png;/4399/1/NRC%2D48078.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: 'acronym identification, supervised learning' lastmod: 2011-03-11 08:56:05 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 319-329 pubdom: FALSE publication: ~ publisher: Springer refereed: TRUE referencetext: |- Adar, E. (2002) S-RAD A Simple and Robust Abbreviation Dictionary, HP Laboratories Technical Report, September. Chang, J.T., Sch�tze, H. and Altman R.B., (2002), Creating an Online Dictionary of Abbreviations from MEDLINE, Journal of American Medical Informatics Association(JAMIA), 9(6), p.612-620. Larkey, L., Ogilvie, P., Price, A. and Tamilio, B. (2000) Acrophile: An Automated Acronym Extractor and Server, In Proceedings of the ACM Digital Libraries conference, pp. 205-214. Park, Y., and Byrd, R.J., (2001), Hybrid Text Mining for Finding Abbreviations and Their Definitions, Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, Pittsburgh, PA. Pustejovsky, J., Castao, J., Cochran, B., Kotecki, M., Morrell, M. and Rumshisky, A. (2001) "Extraction and Disambiguation of Acronym-Meaning Pairs in Medline", unpublished manuscript. Schwartz, A. and Hearst, M. (2003), A simple algorithm for identifying abbreviation definitions in biomedical texts, In Proceedings of the Pacific Symposium on Biocomputing (PSB). Taghva, K. and Gilbreth, J. (1999), Recognizing acronyms and their definitions, International journal on Document Analysis and Recognition, pages 191-198. Tufis, D. and Mason, O. (1998). Tagging Romanian Texts: a Case Study for QTAG, a Language Independent Probabilistic Tagger, Proceedings of the First International Conference on Language Resources and Evaluation (LREC), Spain, p.589-596. Yeates, S. (1999), Automatic extraction of acronyms from text. In Third New Zealand Computer Science Research Students' Conference, pages 117-124. Yu H, Hripcsak G, Friedman C. (2002) Mapping abbreviations to full forms in biomedical articles, Journal of the American Medical Informatics Association (9) 262-272. Witten I, H, and Frank, E. (2000) Data Mining: Practical machine learning tools with Java implementations, Morgan Kaufmann, San Francisco. Zahariev, M. (2004). A (Acronyms), Ph.D. thesis, School of Computing Science, Simon Fraser University. relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 16:59:30 subjects: - comp-sci-lang succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: A Supervised Learning Approach to Acronym Identification type: confpaper userid: 5664 volume: LNCS 3