Acquisition of Linguistic Patterns for Knowledge-Based Information Extraction
IEEE Transactions on Knowledge and Data Engineering
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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A memory-based learning approach to event extraction in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Semantic role labeling of gene regulation events: preliminary results
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
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This paper describes the supervised acquisition of semantic event frames based on a corpus of biomedical abstracts, in which the biological process of E. coli gene regulation has been linguistically annotated by a group of biologists in the EC research project "BOOTStrep". Gene regulation is one of the rapidly advancing areas for which information extraction could boost research. Event frames are an essential linguistic resource for extraction of information from biological literature. This paper presents a specification for linguistic-level annotation of gene regulation events, followed by novel methods of automatic event frame extraction from text. The event frame extraction performance has been evaluated with 10-fold cross validation. The experimental results show that a precision of nearly 50% and a recall of around 20% are achieved. Since the goal of this paper is event frame extraction, rather than event instance extraction, the issue of low recall could be solved by applying the methods to a larger-scale corpus.