Kaushik Nandan


2016

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Semi-supervised Clustering of Medical Text
Pracheta Sahoo | Asif Ekbal | Sriparna Saha | Diego Moll√° | Kaushik Nandan
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a small annotated dataset, semi-supervised clustering can produce clusters that are customized to the application domain. In this paper, we present a semi-supervised clustering technique based on a multi-objective evolutionary algorithm (NSGA-II-clus). We apply this technique to the task of clustering medical publications for Evidence Based Medicine (EBM) and observe an improvement of the results against unsupervised and other semi-supervised clustering techniques.