A Hybrid Approach for Biomedical Entity Name Recognition

Author

Gong, Le-Jun ; Yuan, Yi ; Wei, You-Bing ; Sun, Xiao

Author_Institution

Dept. of Biol. Sci. & Med. Eng., Southeast Univ., Nanjing, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

5

Abstract

Biomedical named entity recognition, an important step, makes preparation for extracting information from biomedical textual resources. This paper presents a hybrid approach to recognize biomedical entity, which includes POS (Part-of-Speech) tagging, rules-based and dictionary-based approach using biomedical ontology. Experiment results show our approach can find untagged biomedical entity name in the GENIA 3.02 corpus for aiding biologist tagging biomedical entity in the biomedical literature and obtain a recall of 66%, a precision of 78% and an F-score 71.5% for the test dataset extracted from the GENIA 3.02 corpus.

Keywords

biology computing; data mining; diseases; genetics; medical information systems; molecular biophysics; ontologies (artificial intelligence); GENIA 3.02 corpus; biomedical entity name recognition; biomedical literature; biomedical ontology; dictionary-based approach; disease; gene; hybrid approach; part-of-speech tagging; rules-based approach; text mining; Biomedical engineering; Data mining; Dictionaries; Diseases; Engineering in medicine and biology; Laboratories; Ontologies; Proteins; Tagging; Text mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4132-7

Electronic_ISBN

978-1-4244-4134-1

Type

conf

DOI

10.1109/BMEI.2009.5302480

Filename

5302480