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UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge

Jari Björne, Suwisa Kaewphan, Tapio Salakoski, UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge. In: Suresh Manandhar, Deniz Yuret (Eds.), Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), 2, 651–659, Association for Computational Linguistics (ACL), 2013.

Abstract:

The DDIExtraction 2013 task in the SemEval
conference concerns the detection of drug
names and statements of drug-drug interactions (DDI) from text. Extraction of DDIs
is important for providing up-to-date knowledge on adverse interactions between co-
administered drugs. We apply the machine
learning based Turku Event Extraction System to both tasks. We evaluate three feature sets, syntactic features derived from deep
parsing, enhanced optionally with features derived from DrugBank or from both DrugBank
and MetaMap. TEES achieves F-scores of
60% for the drug name recognition task and
59% for the DDI extraction task.

BibTeX entry:

@INPROCEEDINGS{inpBjKaSa13a,
  title = {UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge},
  booktitle = {Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)},
  author = {Björne, Jari and Kaewphan, Suwisa and Salakoski, Tapio},
  volume = {2},
  editor = {Manandhar, Suresh and Yuret, Deniz},
  publisher = {Association for Computational Linguistics (ACL)},
  pages = {651–659},
  year = {2013},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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