Integrating linguistic knowledge into a conditional random fieldframework to identify biomedical named entities

  • Authors:

  • Tzong-han Tsai;Wen-Chi Chou;Shih-Hung Wu;Ting-Yi Sung;Jieh Hsiang;Wen-Lian Hsu

  • Affiliations:

  • Institute of Information Science, Acdemia Sinica, Taipei, Taiwan, ROC and Graduate School of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC;Institute of Information Science, Acdemia Sinica, Taipei, Taiwan, ROC;Department and Graduate Institute of CSIE, Chaoyang University of Technology, Taichung County 41349, Taiwan, ROC;Institute of Information Science, Acdemia Sinica, Taipei, Taiwan, ROC;Graduate School of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC;Institute of Information Science, Acdemia Sinica, Taipei, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2006

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Abstract

As new high-throughput technologies have created an explosion of biomedical literature, there arises a pressing need for automatic information extraction from the literature bank. To this end, biomedical named entity recognition (NER) from natural language text is indispensable. Current NER approaches include: dictionary based, rule based, or machine learning based. Since, there is no consolidated nomenclature for most biomedical NEs, any NER system relying on limited dictionaries or rules does not seem to perform satisfactorily. In this paper, we consider a machine learning model, CRF, for the construction of our NER framework. CRF is a well-known model for solving other sequence tagging problems. In our framework, we do our best to utilize available resources including dictionaries, web corpora, and lexical analyzers, and represent them as linguistic features in the CRF model. In the experiment on the JNLPBA 2004 data, with minimal post-processing, our system achieves an F-score of 70.2%, which is better than most state-of-the-art systems. On the GENIA 3.02 corpus, our system achieves an F-score of 78.4% for protein names, which is 2.8% higher than the next-best system. In addition, we also examine the usefulness of each feature in our CRF model. Our experience could be valuable to other researchers working on machine learning based NER.