Semi-supervised clustering of medical text

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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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.
Original languageEnglish
Title of host publicationClinicalNLP 2016 Clinical Natural Language Processing Workshop
Subtitle of host publicationproceedings of the Workshop
Place of PublicationStroudsburg PA
PublisherAssociation for Computational Linguistics
Number of pages9
ISBN (Electronic)9784879747105
Publication statusPublished - 2016
EventClinical Natural Language Processing Workshop - Osaka, Japan
Duration: 11 Dec 201611 Dec 2016


WorkshopClinical Natural Language Processing Workshop
Abbreviated titleClinicalNLP 2016

Bibliographical note

Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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