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Two-phase biomedical named entity recognition using CRFs

Paper ID Volume ID Publish Year Pages File Format Full-Text
15302 1401 2009 5 PDF Available
Title
Two-phase biomedical named entity recognition using CRFs
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.

Keywords
Text mining; Biomedical named entity recognition; Named entity detection; Named entity classification; Conditional random fields
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 33, Issue 4, August 2009, Pages 334–338
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us