Recognizing names in biomedical texts: a machine learning approach | Zendy

Guodong Zhou | Zendy; Jie Zhang | Zendy; Jian Su | Zendy; Dan Shen | Zendy; Chew-Lim Tan | Zendy

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Recognizing names in biomedical texts: a machine learning approach

Author(s) -

Guodong Zhou,

Jie Zhang,

Jian Su,

Dan Shen,

Chew-Lim Tan

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth060

Subject(s) - computer science , artificial intelligence , natural language processing , named entity recognition , suffix , hidden markov model , domain (mathematical analysis) , wordnet , feature (linguistics) , information extraction , information retrieval , task (project management) , linguistics , mathematical analysis , philosophy , mathematics , management , economics

With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery 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.

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