Recognizing names in biomedical texts: a machine learning approach

DMCA

Recognizing names in biomedical texts: a machine learning approach (2004)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Guodong Zhou , Jie Zhang , Jian Su , Dan Shen , Chewlim Tan

Venue:	Bioinformatics
Citations:	52 - 0 self

BibTeX

@ARTICLE{Zhou04recognizingnames,
    author = {Guodong Zhou and Jie Zhang and Jian Su and Dan Shen and Chewlim Tan},
    title = {Recognizing names in biomedical texts: a machine learning approach},
    journal = {Bioinformatics},
    year = {2004},
    pages = {1178--1190}
}

OpenURL

Abstract

Motivation: With an overwhelming amount of textual informa-tion in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge dis-covery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. Results: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential fea-tures: (1) word formation pattern; (2) morphological pattern,

Keyphrases

machine learning approach biomedical text biomedical domain special phenomenon molecular biology entity recognition system biomedical entity name morphological pattern textual informa-tion information retrieval efficient literature mining text document knowledge acquisition word formation pattern various evidential fea-tures overwhelming amount information extraction

DMCA