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Feature selection techniques for maximum entropy based biomedical named entity recognition.

Abstract：

:Named entity recognition is an extremely important and fundamental task of biomedical text mining. Biomedical named entities include mentions of proteins, genes, DNA, RNA, etc which often have complex structures, but it is challenging to identify and classify such entities. Machine learning methods like CRF, MEMM and SVM have been widely used for learning to recognize such entities from an annotated corpus. The identification of appropriate feature templates and the selection of the important feature values play a very important role in the success of these methods. In this paper, we provide a study on word clustering and selection based feature reduction approaches for named entity recognition using a maximum entropy classifier. The identification and selection of features are largely done automatically without using domain knowledge. The performance of the system is found to be superior to existing systems which do not use domain knowledge.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Saha SK,Sarkar S,Mitra P

doi

10.1016/j.jbi.2008.12.012

subject

Has Abstract

pub_date

2009-10-01 00:00:00

pages

905-11

issue

5

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(09)00003-3

journal_volume

42

pub_type

杂志文章

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