Change search
ReferencesLink to record
Permanent link

Direct link
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 (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
URN: urn:nbn:se:su:diva-95577ISBN: 978-1-62748-976-8OAI: diva2:660885
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

Open Access in DiVA

No full text

Other links

Search in DiVA

By author/editor
Skeppstedt, Maria
By organisation
Department of Computer and Systems Sciences
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 43 hits
ReferencesLink to record
Permanent link

Direct link