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RNTI

MODULAD
Biological event extraction using SVM and composite kernel function
In EGC 2012, vol. RNTI-E-23, pp.101-112
Résumé
With an overwhelming of experimental and computational results in
molecular biology, there is an increasing interest to provide tools that will automatically
extract structured biological information recorded in freely available
text. Extraction of named entities such as protein, gene or disease names and
of simple relations of these entities, such as statements of protein-protein interactions
has gained certain success, and now the new focus research has been
moving to higher level of information extraction such as co-reference resolution
and event extraction. It is precisely the last of these tasks which will be focused
in this paper. The biological event template allows detailed representations of
complex natural language statements, which is specified by a trigger and arguments
labeled by semantic roles.
In this paper, we have developed a biological event extraction approach which
uses Support Vector Machines (SVM) and a suitable composite kernel function
to identify triggers and to assign the corresponding arguments. Also, we make
use of a number of features based on both syntactic and contextual information
which where automatically learned from the training data.
We implemented our event extraction system using the state-of-the-art of NLP
tools. We achieved competitive results compared to the BioNLP'09 Shared task
benchmark.