Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx; PyConTextNLP and SynNeg

Hideyuki Tanushi
Dept. Of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden

Hercules Dalianis
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden

Martin Duneld
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden

Maria Kvist
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden and Dept. of clinical immunology and transfusion medicine, Karolinska University Hospital, Stockholm, Sweden

Maria Skeppstedt
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden

Sumithra Velupillai
Dept. of Computer and Systems Sciences (DSV), Stockholm University, Kista, Sweden

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Ingår i: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16

Linköping Electronic Conference Proceedings 85:35, s. 387-397

NEALT Proceedings Series 16:35, s. 387-397

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Publicerad: 2013-05-17

ISBN: 978-91-7519-589-6

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Negation detection is a key component in clinical information extraction systems; as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not); but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cues. We have in this paper compared three different systems for negation detection in Swedish clinical text (NegEx; PyConTextNLP and SynNeg); which have different approaches for determining the scope of negation cues. NegEx uses the distance between the cue and the disease; PyConTextNLP relies on a list of conjunctions limiting the scope of a cue; and in SynNeg the boundaries of the sentence units; provided by a syntactic parser; limit the scope of the cues. The three systems produced similar results; detecting negation with an F-score of around 80%; but using a parser had advantages when handling longer; complex sentences or short sentences with contradictory statements.


Clinical text; negation detection; syntactic analysis


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