Using combinatory categorial grammar to extract biomedical information

Author

Park, Jong C.

Author_Institution

Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Yusong-Gu, South Korea

Volume

16

Issue

6

fYear

2001

Firstpage

62

Lastpage

67

Abstract

Extracting information from biology databases manually can be an overwhelming task. GenBank, the US National Institutes of Health database containing all publicly available DNA sequences, has more than 14 billion bases in 13 million genetic-sequence records. Medline, a literature database available through PubMed, has over 11 million journal citations. In a May 2001 search request for "cytokine" (regulatory proteins in the immune system), PubMed returned 296556 articles. Given the quantity and complexity of biomedical literature, demands for computational tools to extract specific information are increasing. The author reviews biomedical information extraction methods and presents research done by KAIST\´s natural language processing group on a system that shows encouraging performance using combinatory categorial grammar as a natural language grammar formalism.

Keywords

bibliographic systems; category theory; grammars; information retrieval; medical information systems; natural languages; GenBank; KAIST; Medline; PubMed; bioinformatics; biology databases; biomedical information extraction; combinatory categorial grammar; computational tools; genetic-sequence records; literature database; natural language grammar formalism; natural language processing; publicly available DNA sequences; Amino acids; Biomedical measurements; DNA; Data mining; Databases; Electric shock; Muscles; Natural language processing; Natural languages; Proteins;

fLanguage

English

Journal_Title

Intelligent Systems, IEEE

Publisher

ieee

ISSN

1541-1672

Type

jour

DOI

10.1109/5254.972092

Filename

972092