Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Applied morphological processing of English
Natural Language Engineering
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Extracting complex biological events with rich graph-based feature sets
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Reranking the Berkeley and brown parsers
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Event extraction as dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Any domain parsing: automatic domain adaptation for natural language parsing
Any domain parsing: automatic domain adaptation for natural language parsing
Overview of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of Genia event task in BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of the infectious diseases (ID) task of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Model combination for event extraction in BioNLP 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task 2011: supporting resources
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of Genia event task in BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of the infectious diseases (ID) task of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Robust biomedical event extraction with dual decomposition and minimal domain adaptation
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Model combination for event extraction in BioNLP 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Fast and robust joint models for biomedical event extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics
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We describe the Stanford entry to the BioNLP 2011 shared task on biomolecular event extraction (Kim et al., 2011a). Our framework is based on the observation that event structures bear a close relation to dependency graphs. We show that if biomolecular events are cast as these pseudosyntactic structures, standard parsing tools (maximum-spanning tree parsers and parse rerankers) can be applied to perform event extraction with minimum domain-specific tuning. The vast majority of our domain-specific knowledge comes from the conversion to and from dependency graphs. Our system performed competitively, obtaining 3rd place in the Infectious Diseases track (50.6% f-score), 5th place in Epigenetics and Post-translational Modifications (31.2%), and 7th place in Genia (50.0%). Additionally, this system was part of the combined system in Riedel et al. (2011) to produce the highest scoring system in three out of the four event extraction tasks.