Semi-supervised Clustering of Medical Text

Pracheta Sahoo, Asif Ekbal, Sriparna Saha, Diego Mollá, Kaushik Nandan


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.
Anthology ID:
W16-4205
Volume:
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann
Venue:
ClinicalNLP
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
23–31
Language:
URL:
https://aclanthology.org/W16-4205
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Semi-supervised Clustering of Medical Text (Sahoo et al., ClinicalNLP 2016)
Copy Citation:
PDF:
https://aclanthology.org/W16-4205.pdf