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Extracting Complex Biological Events with Rich Graph-Based Feature Sets

Jari Björne, Juho Heimonen, Filip Ginter, Antti Airola, Tapio Pahikkala, Tapio Salakoski, Extracting Complex Biological Events with Rich Graph-Based Feature Sets. In: Jun'ichi Tsujii (Ed.), Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task, 10–18, Association for Computational Linguistics (ACL), 2009.

Abstract:

We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP’09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95% on the primary task, the best performance among the participants.

BibTeX entry:

@INPROCEEDINGS{inpBjHeGiAiPaSa09a,
  title = {Extracting Complex Biological Events with Rich Graph-Based Feature Sets},
  booktitle = {Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task},
  author = {Björne, Jari and Heimonen, Juho and Ginter, Filip and Airola, Antti and Pahikkala, Tapio and Salakoski, Tapio},
  editor = {Tsujii, Jun'ichi},
  publisher = {Association for Computational Linguistics (ACL)},
  pages = {10–18},
  year = {2009},
}

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

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