TY - GEN
T1 - Model combination for event extraction in BioNLP 2011
AU - Riedel, Sebastian
AU - McClosky, David
AU - Surdeanu, Mihai
AU - McCallum, Andrew
AU - Manning, Christopher D.
N1 - Funding Information:
We thank the BioNLP shared task organizers for setting this up and their quick responses to questions. This work was supported in part by the Center for Intelligent Information Retrieval. We gratefully acknowledge the support of the Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0181.
Publisher Copyright:
© 2011 Association for Computational Linguistics.
PY - 2011
Y1 - 2011
N2 - 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%).
AB - 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%).
UR - http://www.scopus.com/inward/record.url?scp=85017492021&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017492021&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85017492021
T3 - Proceedings of BioNLP Shared Task 2011 Workshop at the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL HLT 2011
SP - 51
EP - 55
BT - Proceedings of BioNLP Shared Task 2011 Workshop at the 49th Annual Meeting of the Association for Computational Linguistics
A2 - Tsujii, Jun'ichi
A2 - Kim, Jin-Dong
A2 - Pyysalo, Sampo
PB - Association for Computational Linguistics (ACL)
T2 - BioNLP Shared Task 2011 Workshop at the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL HLT 2011
Y2 - 24 June 2011
ER -