From pathways to biomolecular events: opportunities and challenges
BioNLP '11 Proceedings of BioNLP 2011 Workshop
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 2011: supporting resources
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Extracting biological events from text using simple syntactic patterns
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Efficient matrix-encoded grammars and low latency parallelization strategies for CYK
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
New resources and perspectives for biomedical event extraction
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Supervised hypothesis discovery using syllogistic patterns in the biomedical literature
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Motivation: There has recently been a notable shift in biomedical information extraction (IE) from relation models toward the more expressive event model, facilitated by the maturation of basic tools for biomedical text analysis and the availability of manually annotated resources. The event model allows detailed representation of complex natural language statements and can support a number of advanced text mining applications ranging from semantic search to pathway extraction. A recent collaborative evaluation demonstrated the potential of event extraction systems, yet there have so far been no studies of the generalization ability of the systems nor the feasibility of large-scale extraction. Results: This study considers event-based IE at PubMed scale. We introduce a system combining publicly available, state-of-the-art methods for domain parsing, named entity recognition and event extraction, and test the system on a representative 1% sample of all PubMed citations. We present the first evaluation of the generalization performance of event extraction systems to this scale and show that despite its computational complexity, event extraction from the entire PubMed is feasible. We further illustrate the value of the extraction approach through a number of analyses of the extracted information. Availability: The event detection system and extracted data are open source licensed and available at http://bionlp.utu.fi/. Contact: jari.bjorne@utu.fi