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Advancing Biomedical Named Entity Recognition with Multivariate Feature Selection and Semantically Motivated Features

Abstract Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and pro... (more)
Created Date 2013
Contributor Leaman, James Robert (Author) / Gonzalez, Graciela (Advisor) / Baral, Chitta (Advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computational linguistics / Information extraction / Natural language processing
Type Doctoral Dissertation
Extent 120 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Computer Science 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis