Abstract
In this paper, Support Vector Machines (SVMs) are applied to the identification and automatic annotation of biomedical named entities in the domain of molecular biology, as an extension of the traditional named entity recognition task to special domains. The effect of the use of well-known features such as word formation patterns, lexical, morphological, and surface words on recognition performance is investigated. Experiments have been conducted using the train and test data made public at the Bio-Entity Recognition Task at JNLPBA 2004. An F-score of 69.87% was obtained by using a carefully selected combination of a minimal set of features, which can be easily computed from training data without any use of post-processing or external resources.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Chichester (2001)
Kudo, T., Matsumoto, Y.: Chunking with Support Vector Machines. In: Proceedings of Second Meeting of North American Chapter of the Association for Computational Linguistics (NAACL), pp. 192–199 (2001)
Zhou, G., Su, J.: Exploring Deep Knowledge Resources in Biomedical Name Recognition. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA 2004), Geneva, Switzerland, pp. 96–99 (2004)
Manning, C.D., Schutze, H.: Foundations of Statistical Language Processing. MIT Press, Cambridge, Massachusetts (1999)
Ohta, T., Tatishi, Y., Mima, H., Tsujii, J.: The GENIA corpus: an annotated research abstract corpus in the molecular biology domain. In: Human Language Technologies Conference (HLT-2002), San Diego, pp. 489–493 (2002)
Collier, N., Takeuchi, K.: Comparison of Character-level and part of speech features for name recognition in biomedical texts. Journal of Biomedical Informatics 37, 423–435 (2004)
Zhou, G., Zhang, J., Su, J., Shen, D., Tan, C.: Recognizing Names in biomedical texts: a machine learning approach. Bioinformatics, 1178–1190 (2003)
Mayfield, J., McNamee, P., Piatko, C.: Named Entity Recognition using Hundreds of Thousands of Features. In: Proceedings of CoNLL-2003, Edmonton, Canada, pp. 184–187 (2003)
van Rijsbergen, C.J.: Information retrieval, 2nd edn. Butterworths, London (1979)
Finkel, J., Dingare, S., Nguyen, H., Nissim, M., Sinclair, G., Manning, C.: Exploiting Text for Biomedical Entity Recognition: From Syntax to the Web. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA 2004), Geneva, Switzerland, pp. 88–91 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dimililer, N., Varoğlu, E. (2006). Recognizing Biomedical Named Entities Using SVMs: Improving Recognition Performance with a Minimal Set of Features. In: Bremer, E.G., Hakenberg, J., Han, EH.(., Berrar, D., Dubitzky, W. (eds) Knowledge Discovery in Life Science Literature. KDLL 2006. Lecture Notes in Computer Science(), vol 3886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11683568_5
Download citation
DOI: https://doi.org/10.1007/11683568_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32809-4
Online ISBN: 978-3-540-32810-0
eBook Packages: Computer ScienceComputer Science (R0)