Skip to main content

A Supervised Approach for Gene Mention Detection

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7076))

Included in the following conference series:

Abstract

Named Entity Recognition and Classification (NERC) is one of the most fundamental and important tasks in biomedical information extraction. Gene mention detection is concerned with the named entity (NE) extraction of gene and gene product mentions in text. Several different approaches have emerged but most of these state-of-the-art approaches suggest that individual NERC system may not cover entity representations with arbitrary set of features and cannot achieve best performance. In this paper, we propose a voted approach for gene mention detection. We use support vector machine (SVM) as the underlying classification methodology, and build different models of it depending upon the various representations of the set of features. One most important criterion of these features is that these are identified and selected largely without using any domain knowledge. Evaluation results with the benchmark dataset of GENTAG yields the state-of-the-art performance with the overall recall, precision and F-measure values of 94.95%, 94.32%, and 94.63%, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aronson, A.R., Bodenreider, O., Chang, H.F., Humphrey, S.M., Mork, J.G., Nelson, S.J., Rindflesch, T.C., Wilbur, W.J.: The NLM Indexing Initiative. In: Proceedings of 2000 AMIA Annual Fall Symposium (2000)

    Google Scholar 

  2. Finkel, J., Dingare, S., Manning, C., Nissim, M., Alex, B., Grover, C.: Exploring the boundaries: gene and protein identification in biomedical text. BMC Bioinformatics 6 (2005)

    Google Scholar 

  3. Hirschman, L., Yeh, A., Blaschke, C., Valencia, A.: Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics 6 (2005)

    Google Scholar 

  4. Joachims, T.: Making Large Scale SVM Learning Practical, pp. 169–184. MIT Press, Cambridge (1999)

    Google Scholar 

  5. Taira, H., Haruno, M.: Feature Selection in SVM Text Categorization. In: Proceedings of AAAI 1999 (1999)

    Google Scholar 

  6. Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the Conll-2003 Shared Task: Language Independent Named Entity Recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp. 142–147 (2003)

    Google Scholar 

  7. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc. (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Saha, S., Ekbal, A., Saha, S. (2011). A Supervised Approach for Gene Mention Detection. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27172-4_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27171-7

  • Online ISBN: 978-3-642-27172-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics