%0 Conference Paper %1 bjorne2009 %A Björne, Jari %A Heimonen, Juho %A Ginter, Filip %A Airola, Antti %A Pahikkala, Tapio %A Salakoski, Tapio %B Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task %C Boulder, Colorado %D 2009 %I Association for Computational Linguistics %K biomedical bionlp09 corpus_genia event_extraction opensource %P 10--18 %T Extracting Complex Biological Events with Rich Graph-Based Feature Sets %U http://www.aclweb.org/anthology/W09-1402

@inproceedings{bjorne2009, added-at = {2009-10-22T15:06:46.000+0200}, address = {Boulder, Colorado}, author = {Bj\"{o}rne, Jari and Heimonen, Juho and Ginter, Filip and Airola, Antti and Pahikkala, Tapio and Salakoski, Tapio}, biburl = {https://www.bibsonomy.org/bibtex/24b2f7bac540367271114f939e0474481/jnothman}, booktitle = {Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task}, description = {Treat task as finding nodes (events, entities) and edges (args) in a graph. Trigger detection: SVM word tagger; known multiword triggers from training are postprocessed; for cases where tokens have multiple annotations, create new double-annotation classes; postprocessing handles multiple events of the same type sharing a trigger. Large feature space. Introduce β multiplier for negative class to calibrate precision-recall tradeoff for whole system. Multi-class SVM for argument classification with many features, based on *groupings* of tokens along the shortest dependency path between candidate trigger and arg. Each pair-classification decision is independent. Rule-based post-processing on graph produces valid events. Had considered N-best reranking of candidate graphs, but couldn't build a system of any effect (it has potential of 11.5% F-score improvement). System errors almost evenly split between trigger and edge detectors. Ignore multi-sentence annotations, since 95% of all events are in 1 sentence. Intend to open-source their system.}, interhash = {124dbb1964c59d19eaaf5e360fa65c9e}, intrahash = {4b2f7bac540367271114f939e0474481}, keywords = {biomedical bionlp09 corpus_genia event_extraction opensource}, month = {June}, pages = {10--18}, publisher = {Association for Computational Linguistics}, timestamp = {2009-10-22T15:06:46.000+0200}, title = {Extracting Complex Biological Events with Rich Graph-Based Feature Sets}, url = {http://www.aclweb.org/anthology/W09-1402}, year = 2009 }