Improving phenotype name recognition

Authors:
Maryam Khordad;Robert E. Mercer;Peter Rogan
Affiliations:
Department of Computer Science, The University of Western Ontario, London, ON, Canada;Department of Computer Science, The University of Western Ontario, London, ON, Canada;Department of Computer Science, The University of Western Ontario, London, ON, Canada and Department of BiochemistryThe University of Western Ontario, London, ON, Canada
Venue:
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Year:
2011

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Abstract

Due to the rapidly increasing amount of biomedical literature, automatic processing of biomedical papers is extremely important. Named Entity Recognition (NER) in this type of writing has several difficulties. In this paper we present a system to find phenotype names in biomedical literature. The system is based on Metamap and makes use of the UMLS Metathesaurus and the Human Phenotype Ontology. From an initial basic system that uses only these preexisting tools, five rules that capture stylistic and linguistic properties of this type of literature are proposed to enhance the performance of our NER tool. The tool is tested on a small corpus and the results (precision 97.6% and recall 88.3%) demonstrate its performance.