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Sentence identification of biological interactions using PATRICIA tree generated patterns and genetic algorithm optimized parameters
- Source :
-
Data & Knowledge Engineering . Jan2010, Vol. 69 Issue 1, p137-152. 16p. - Publication Year :
- 2010
-
Abstract
- Abstract: An important task in information retrieval is to identify sentences that contain important relationships between key concepts. In this work, we propose a novel approach to automatically extract sentence patterns that contain interactions involving concepts of molecular biology. A pattern is defined in this work as a sequence of specialized Part-of-Speech (POS) tags that capture the structure of key sentences in the scientific literature. Each candidate sentence for the classification task is encoded as a POS array and then aligned to a collection of pre-extracted patterns. The quality of the alignment is expressed as a pairwise alignment score. The most innovative component of this work is the use of a genetic algorithm (GA) to maximize the classification performance of the alignment scoring scheme. The system achieves an average F-score of 0.796 in identifying sentences which describe interactions between co-occurring biological concepts. This performance is mostly affected by the quality of the preprocessing steps such as term identification and POS tagging. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 0169023X
- Volume :
- 69
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Data & Knowledge Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 45544537
- Full Text :
- https://doi.org/10.1016/j.datak.2009.09.002