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EACL - Expansion of Abbreviations in CLinical text
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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2014 (English)In: Proceedings of the 3rdWorkshop on Predicting and Improving Text Readability for Target Reader Population, Association for Computational Linguistics , 2014Conference paper, Published paper (Refereed)
Abstract [en]

In the medical domain, especially in clinical texts, non-standard abbreviations are prevalent, which impairs readability for patients. To ease the understanding of the physicians’ notes, abbreviations need to be identified and expanded to their original forms. We present a distributional semantic approach to find candidates of the original form of the abbreviation, and combine this with Levenshtein distance to choose the correct candidate among the semantically related words. We apply the method to radiology reports and medical journal texts, and compare the results to general Swedish. The results show that the correct expansion of the abbreviation can be found in 40% of the cases, an improvement by 24 percentage points compared to the baseline (0.16), and an increase by 22 percentage points compared to using word space models alone (0.18).

Place, publisher, year, edition, pages
Association for Computational Linguistics , 2014.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-110977ISBN: 978-1-937284-91-6 (print)OAI: oai:DiVA.org:su-110977DiVA: diva2:773751
Conference
14th Conference of the European Chapter of the Association for Computational Linguistics,April 27, 2014 Gothenburg, Sweden
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2015-03-10Bibliographically approved

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Henriksson, AronDuneld, MartinKvist, Maria
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