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CBR-Tagger: a case-based reasoning approach to the gene/protein mention problem

Published:19 June 2008Publication History

ABSTRACT

This work proposes a case-based classifier to tackle the gene/protein mention problem in biomedical literature. The so called gene mention problem consists of the recognition of gene and protein entities in scientific texts. A classification process aiming at deciding if a term is a gene mention or not is carried out for each word in the text. It is based on the selection of the best or most similar case in a base of known and unknown cases. The approach was evaluated on several datasets for different organisms and results show the suitability of this approach for the gene mention problem.

References

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  2. Hirschman, L., Colosimo, M., Morgan, A.,&Yeh, A. (2005). Overview of BioCreAtIvE task 1B: normalized gene lists. BMC Bioinformatics, 6 Suppl 1, S11.Google ScholarGoogle ScholarCross RefCross Ref
  3. Morgan, A.,&Hirschman, L. (2007). Overview of Bio-Creative II Gene Normalization. Paper presented at the Second BioCreative Challenge Evaluation Workshop, Madrid-Spain.Google ScholarGoogle Scholar
  4. Wilbur, J., Smith, L.,&Tanabe, L. (2007). BioCreative 2. Gene Mention Task. Paper presented at the Second BioCreative Challenge Evaluation Workshop, Madrid, Spain.Google ScholarGoogle Scholar

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  1. CBR-Tagger: a case-based reasoning approach to the gene/protein mention problem

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              • Published in

                cover image DL Hosted proceedings
                BioNLP '08: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
                June 2008
                135 pages
                ISBN:9781932432114

                Publisher

                Association for Computational Linguistics

                United States

                Publication History

                • Published: 19 June 2008

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                • research-article

                Acceptance Rates

                BioNLP '08 Paper Acceptance Rate10of34submissions,29%Overall Acceptance Rate33of92submissions,36%

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