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Annotating named entities in clinical text by combining pre-annotation and active learning
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2013 (English)In: 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013): Student Research Workshop, Association for Computational Linguistics, 2013, 74-80 p.Conference paper, Published paper (Refereed)
Abstract [en]

Sentence types typical to Swedish clini- cal text were extracted by comparing sen- tence part-of-speech tag sequences in clin- ical and in standard Swedish text. Parsings by a syntactic dependency parser, trained on standard Swedish, were manually ana- lysed for the 33 sentence types most typ- ical to clinical text. This analysis re- sulted in the identification of eight error types, and for two of these error types, pre- processing rules were constructed to im- prove the performance of the parser. For all but one of the ten sentence types af- fected by these two rules, the parsing was improved by pre-processing.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2013. 74-80 p.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-95577ISBN: 978-1-62748-976-8 (print)OAI: oai:DiVA.org:su-95577DiVA: diva2:660885
Conference
ACL 2013: The 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, August 4-9 2013
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2013-11-06Bibliographically approved

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http://aclweb.org/anthology/P/P13/P13-3011.pdf

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Skeppstedt, Maria
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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf