He, Yulan and Chenghua, Lin
(2009).
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| URL: | http://www.springerlink.com/content/978-3-642-1254... |
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| DOI (Digital Object Identifier) Link: | http://dx.doi.org/doi:10.1007/978-3-642-12550-8_15 |
| Google Scholar: | Look up in Google Scholar |
Abstract
Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semi-supervised learning algorithms such as SVM and it also performs better than local learning without incorporating class priors.
| Item Type: | Conference Item |
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| Copyright Holders: | 2009 Springer-Verlag |
| ISSN: | 0302-9743 |
| Extra Information: | Natural Language Processing and Information Systems
14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers Helmut Horacek, Elisabeth Métais, Rafael Muñoz and Magdalena Wolska ISBN 978-3-642-12549-2 Lecture Notes in Computer Science Volume 5723, 2010 |
| Academic Unit/Department: | Knowledge Media Institute |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Related URLs: | |
| Item ID: | 24607 |
| Depositing User: | Yulan He |
| Date Deposited: | 07 Dec 2010 09:07 |
| Last Modified: | 12 May 2013 12:30 |
| URI: | http://oro.open.ac.uk/id/eprint/24607 |
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