Model combination for event extraction in BioNLP 2011

  • Authors:
  • Sebastian Riedel;David McClosky;Mihai Surdeanu;Andrew McCallum;Christopher D. Manning

  • Affiliations:
  • University of Massachusetts at Amherst;Stanford University;Stanford University;University of Massachusetts at Amherst;Stanford University

  • Venue:
  • BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
  • Year:
  • 2011

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Abstract

We describe the FAUST entry to the BioNLP 2011 shared task on biomolecular event extraction. The FAUST system explores several stacking models for combination using as base models the UMass dual decomposition (Riedel and McCallum, 2011) and Stanford event parsing (McClosky et al., 2011b) approaches. We show that using stacking is a straightforward way to improving performance for event extraction and find that it is most effective when using a small set of stacking features and the base models use slightly different representations of the input data. The FAUST system obtained 1st place in three out of four tasks: 1st place in Genia Task 1 (56.0% f-score) and Task 2 (53.9%), 2nd place in the Epigenetics and Post-translational Modifications track (35.0%), and 1st place in the Infectious Diseases track (55.6%).