Paper Info
Title
Event extraction with complex event classification using rich features.
Abstract
Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge.
Year
DOI
Venue
2010
10.1142/S0219720010004586
J. Bioinformatics and Computational Biology
Keywords
Field
DocType
support vector machine
Data mining,Binary relation,Computer science,Support vector machine,Complex event processing,Biomedical text mining,Artificial intelligence,Bioinformatics,Machine learning
Journal
Volume
Issue
ISSN
8
1
1757-6334
Citations 
PageRank 
References 
98
3.24
18
Authors
4
Name
Order
Citations
PageRank
Makoto Miwa174644.93
Rune Sætre256028.49
Jin-Dong Kim3170592.21
Jun-ichi Tsujii41973219.85