@inproceedings{sahoo-etal-2016-semi,
title = "Semi-supervised Clustering of Medical Text",
author = "Sahoo, Pracheta and
Ekbal, Asif and
Saha, Sriparna and
Moll{\'a}, Diego and
Nandan, Kaushik",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4205",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Clustering of Medical Text
%A Sahoo, Pracheta
%A Ekbal, Asif
%A Saha, Sriparna
%A Mollá, Diego
%A Nandan, Kaushik
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F sahoo-etal-2016-semi
%X 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.
%U https://aclanthology.org/W16-4205
%P 23-31
Markdown (Informal)
[Semi-supervised Clustering of Medical Text](https://aclanthology.org/W16-4205) (Sahoo et al., ClinicalNLP 2016)
ACL
- Pracheta Sahoo, Asif Ekbal, Sriparna Saha, Diego Mollá, and Kaushik Nandan. 2016. Semi-supervised Clustering of Medical Text. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 23–31, Osaka, Japan. The COLING 2016 Organizing Committee.