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Drug-Drug Interaction Extraction with RLS and SVM Classifiers

Jari Björne, Antti Airola, Tapio Pahikkala, Tapio Salakoski, Drug-Drug Interaction Extraction with RLS and SVM Classifiers. In: Isabel Segura-Bedmar, Paloma Martinez, Daniel Sanchez-Cisneros (Eds.), Proceedings of the First Challenge task on Drug- Drug Interaction Extraction (DDIExtraction 2011), Ceur Workshop Proceedings, 35–42, CEUR Workshop Proceedings, 2011.

Abstract:

We introduce a system developed to extract drug-drug in-
teractions (DDI) for drug mention pairs found in biomedical texts. This
system was developed for the DDI Extraction First Challenge Task 2011
and is based on our publicly available Turku Event Extraction System,
which we adapt for the domain of drug-drug interactions. This system
relies heavily on deep syntactic parsing to build a representation of the
relations between drug mentions. In developing the DDI extraction sys-
tem, we evaluate the suitability of both text-based and database derived
features for DDI detection. For machine learning, we test both support
vector machine (SVM) and regularized least-squares (RLS) classifiers,
with detailed experiments for determining the optimal parameters and
training approach. Our system achieves a performance of 62.99% F-score
on the DDI Extraction 2011 task.

BibTeX entry:

@INPROCEEDINGS{inpBjAiPaSaxxa,
  title = {Drug-Drug Interaction Extraction with RLS and SVM Classifiers},
  booktitle = {Proceedings of the First Challenge task on Drug- Drug Interaction Extraction (DDIExtraction 2011)},
  author = {Björne, Jari and Airola, Antti and Pahikkala, Tapio and Salakoski, Tapio},
  series = {Ceur Workshop Proceedings},
  editor = {Segura-Bedmar, Isabel and Martinez, Paloma and Sanchez-Cisneros, Daniel},
  publisher = {CEUR Workshop Proceedings},
  pages = {35–42},
  year = {2011},
  keywords = {drug-drug interaction extraction, text mining},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Turku BioNLP Group

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