@InProceedings{sahoo-EtAl:2016:ClinicalNLP,
  author    = {Sahoo, Pracheta  and  Ekbal, Asif  and  Saha, Sriparna  and  Molla, Diego  and  Nandan, Kaushik},
  title     = {Semi-supervised Clustering of Medical Text},
  booktitle = {Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {23--31},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/W16-4205}
}