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Complex event extraction at PubMed scale
- Source :
- Bioinformatics
- Publication Year :
- 2010
- Publisher :
- Oxford University Press, 2010.
-
Abstract
- 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
- Subjects :
- Statistics and Probability
PubMed
Relation (database)
Computer science
Text Mining
computer.software_genre
Biochemistry
Text mining
Named-entity recognition
Data Mining
Molecular Biology
Natural Language Processing
Parsing
Information retrieval
business.industry
Event (computing)
Systems Biology
Semantic search
Relationship extraction
Biomedical text mining
Ismb 2010 Conference Proceedings July 11 to July 13, 2010, Boston, Ma, Usa
Original Papers
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
business
computer
Natural language
Subjects
Details
- Language :
- English
- ISSN :
- 13674811 and 13674803
- Volume :
- 26
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....c2969b8da239abb62439c83e7b41ec59