Journal
BIOINFORMATICS
Volume 26, Issue 5, Pages 661-667Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq002
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Funding
- Pfizer Ltd.
- Joint Information Systems Committee
- BBSRC [BB/G013160/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/G013160/1] Funding Source: researchfish
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Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers. Results: We build a corpus for organism disambiguation where every occurrence of protein/gene entity is manually tagged with a species ID, and evaluate a number of methods on it. Promising results are obtained by training a machine learning model on syntactic parse trees, which is then used to decide whether an entity belongs to the model organism denoted by a neighbouring species-indicating word (e.g. yeast). The parser-based approaches are also compared with a supervised classification method and results indicate that the former are a more favorable choice when domain portability is of concern. The best overall performance is obtained by combining the strengths of syntactic features and supervised classification.
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