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Various Features with Integrated Strategies for Protein Name Classification

  • Budi Taruna Ongkowijaya
  • Shilin Ding
  • Xiaoyan Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3759)

Abstract

Classification task is an integral part of named entity recognition system to classify a recognized named entity to its corresponding class. This task has not received much attention in the biomedical domain, due to the lack of awareness to differentiate feature sources and strategies in previous studies. In this research, we analyze different sources and strategies of protein name classification, and developed integrated strategies that incorporate advantages from rule-based, dictionary-based and statistical-based method. In rule-based method, terms and knowledge of protein nomenclature that provide strong cue for protein name are used. In dictionary-based method, a set of rules for curating protein name dictionary are used. These terms and dictionaries are combined with our developed features into a statistical-based classifier. Our developed features are comprised of word shape features and unigram & bi-gram features. Our various information sources and integrated strategies are able to achieve state-of-the-art performance to classify protein and non-protein names.

Keywords

Classification Task Integrate Strategy Regular Expression External Information Name Entity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Budi Taruna Ongkowijaya
    • 1
  • Shilin Ding
    • 1
  • Xiaoyan Zhu
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems (LITS), Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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