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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W16-4205.pdf