Authors: Yang, Zhihao; Lin, Hongfei; Li, Yanpeng

Source: Journal of Computational and Theoretical Nanoscience, Volume 4, Numbers 7-8, November/December 2007 , pp. 1426-1431(6)

Publisher: American Scientific Publishers

Abstract:

To extract biomedical information about bio-entities from the huge amount of biomedical literature, the first key step is recognizing the bio-entity names in literature, which remains a challenging task due to the irregularities and ambiguities in bio-entities nomenclature. Currently the performance of the most popular methods, machine learning techniques, still have much room for improvement. This paper presents a Conditional Random Fields-based bio-entity name recognition approach and studies the methods of improving the performance by exploiting contextual cues and other post-processing methods. Experiment results show that these methods improve Conditional Random Fields-based recognition performance.

Keywords: TEXT MINING; BIO-ENTITY RECOGNITION; CONDITIONAL RANDOM FIELDS

Document Type: Research article

DOI: http://dx.doi.org/10.1166/jctn.2007.035

Publication date: 2007-11-01

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• Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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