Relation extraction for drug-drug interactions using ensemble learning

Philippe Thomas, Mariana Neves, Illés Solt, Domonkos Tikk, Ulf Leser

Research output: Contribution to journalConference article

31 Citations (Scopus)

Abstract

We describe our approach for the extraction of drug-drug interactions from literature. The proposed method builds majority voting ensembles of contrasting machine learning methods, which exploit different linguistic feature spaces. We evaluated our approach in the context of the DDI Extraction 2011 challenge, where using document-wise crossvalidation, the best single classifier achieved an F1 of 57.3% and the best ensemble achieved 60.6%. On the held out test set, our best run achieved an F1 of 65.7%.

Original languageEnglish
Pages (from-to)11-18
Number of pages8
JournalCEUR Workshop Proceedings
Volume761
Publication statusPublished - Dec 1 2011
Event1st Challenge Task on Drug-Drug Interaction Extraction 2011, DDIExtraction 2011 - Co-located with the 27th Conference of the Spanish Society for Natural Language Processing, SEPLN 2011 - Huelva, Spain
Duration: Sep 7 2011Sep 7 2011

Keywords

  • Ensemble learning
  • Machine learning
  • Relation extraction
  • Text mining

ASJC Scopus subject areas

  • Computer Science(all)

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