Semi-supervised clustering of medical text

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

Semi-supervised clustering of medical text

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

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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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.

Original language	English
Title of host publication	ClinicalNLP 2016 Clinical Natural Language Processing Workshop
Subtitle of host publication	proceedings of the Workshop
Place of Publication	Stroudsburg PA
Publisher	Association for Computational Linguistics
Pages	23-31
Number of pages	9
ISBN (Electronic)	9784879747105
Publication status	Published - 2016
Event	Clinical Natural Language Processing Workshop - Osaka, Japan Duration: 11 Dec 2016 → 11 Dec 2016

Workshop

Workshop	Clinical Natural Language Processing Workshop
Abbreviated title	ClinicalNLP 2016
Country/Territory	Japan
City	Osaka
Period	11/12/16 → 11/12/16

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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Publisher versionFinal published version, 408 KBLicence: CC BY

Cite this

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title = "Semi-supervised clustering of medical text",

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 EvidenceBased Medicine (EBM) and observe an improvement of the results against unsupervised and other semi-supervised clustering techniques.",

author = "Pracheta Sahoo and Asif Ekbal and Sriparna Saha and Diego Molla and Kaushik Nandan",

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.; Clinical Natural Language Processing Workshop, ClinicalNLP 2016 ; Conference date: 11-12-2016 Through 11-12-2016",

year = "2016",

language = "English",

pages = "23--31",

booktitle = "ClinicalNLP 2016 Clinical Natural Language Processing Workshop",

publisher = "Association for Computational Linguistics",

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Semi-supervised clustering of medical text. / Sahoo, Pracheta; Ekbal, Asif; Saha, Sriparna et al.
ClinicalNLP 2016 Clinical Natural Language Processing Workshop: proceedings of the Workshop . Stroudsburg PA: Association for Computational Linguistics, 2016. p. 23-31.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Semi-supervised clustering of medical text

AU - Sahoo, Pracheta

AU - Ekbal, Asif

AU - Saha, Sriparna

AU - Molla, Diego

AU - Nandan, Kaushik

N1 - 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.

PY - 2016

Y1 - 2016

N2 - 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 EvidenceBased Medicine (EBM) and observe an improvement of the results against unsupervised and other semi-supervised clustering techniques.

AB - 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 EvidenceBased Medicine (EBM) and observe an improvement of the results against unsupervised and other semi-supervised clustering techniques.

M3 - Conference proceeding contribution

SP - 23

EP - 31

BT - ClinicalNLP 2016 Clinical Natural Language Processing Workshop

PB - Association for Computational Linguistics

CY - Stroudsburg PA

T2 - Clinical Natural Language Processing Workshop

Y2 - 11 December 2016 through 11 December 2016

ER -

Semi-supervised clustering of medical text

Abstract

Workshop

Bibliographical note

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