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Indexed by:期刊论文

Date of Publication:2009-08-01

Journal:COMPUTATIONAL BIOLOGY AND CHEMISTRY

Included Journals:SCIE、EI、PubMed、Scopus

Volume:33

Issue:4

Page Number:334-338

ISSN No.:1476-9271

Key Words:Text mining; Biomedical named entity recognition; Named entity detection; Named entity classification; Conditional random fields

Abstract:As a fundamental step of biomedical text mining, Biomedical Named Entity Recognition (Bio-NER) remains a challenging task. This paper explores a so-called two-phase approach to identify biomedical entities, in which the recognition task is divided into two subtasks: Named Entity Detection (NED) and Named Entity Classification (NEC). And the two subtasks are finished in two phases. At the first phase, we try to identify each named entity with a Conditional Random Fields (CRFs) model without identifying its type; at the second phase, another CRFs model is used to determine the correct entity type for each identified entity. This treatment can reduce the training time significantly and furthermore, more relevant features can be selected for each subtask. In order to achieve a better performance, post-processing algorithms are employed before NEC subtask. Experiments conducted on JNLPBA2004 datasets show that our two-phase approach can achieve an F-score of 74.31%, which outperforms most of the state-of-the-art systems. (C) 2009 Elsevier Ltd. All rights reserved.