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.
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He, Y., Lin, C. (2010). Protein-Protein Interactions Classification from Text via Local Learning with Class Priors. In: Horacek, H., Métais, E., Muñoz, R., Wolska, M. (eds) Natural Language Processing and Information Systems. NLDB 2009. Lecture Notes in Computer Science, vol 5723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12550-8_15
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DOI: https://doi.org/10.1007/978-3-642-12550-8_15
Publisher Name: Springer, Berlin, Heidelberg
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