Multi-objective optimization for clustering of medical publications

Asif Ekbal; Sriparna Saha; Diego Mollá; K. Ravikumar

Multi-objective optimization for clustering of medical publications

Asif Ekbal, Sriparna Saha, Diego Mollá, K. Ravikumar

School of Computing

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

2 Citations (Scopus)

Abstract

Clustering the results of a search can help a multi-document summarizer present a summary for evidence based medicine (EBM). In this work, we introduce a clustering technique that is based on multiobjective (MOO) optimization. MOO is a technique that shows promise in the areas of machine learning and natural language processing. In our approach we show how MOO based semi-supervised clustering technique can be effectively used for EBM.

Original language	English
Title of host publication	ALTA 2013
Subtitle of host publication	Proceedings of the Australasian Language Technology Association Workshop
Editors	Sarvnaz Karimi, Karin Verspoor
Place of Publication	Brisbane
Publisher	Australasian Language Technology Association
Pages	53-61
Number of pages	9
Publication status	Published - 2013
Event	Australasian Language Technology Workshop (11th : 2013) - Brisbane Duration: 4 Dec 2013 → 6 Dec 2013

Workshop

Workshop	Australasian Language Technology Workshop (11th : 2013)
City	Brisbane
Period	4/12/13 → 6/12/13

Access to Document

http://aclweb.org/anthology//U/U13/U13-1008.pdf

Cite this

@inproceedings{5fe14d21c24247be8a0b43b772b5614d,

title = "Multi-objective optimization for clustering of medical publications",

abstract = "Clustering the results of a search can help a multi-document summarizer present a summary for evidence based medicine (EBM). In this work, we introduce a clustering technique that is based on multiobjective (MOO) optimization. MOO is a technique that shows promise in the areas of machine learning and natural language processing. In our approach we show how MOO based semi-supervised clustering technique can be effectively used for EBM.",

author = "Asif Ekbal and Sriparna Saha and Diego Moll{\'a} and K. Ravikumar",

year = "2013",

language = "English",

pages = "53--61",

editor = "Sarvnaz Karimi and Karin Verspoor",

booktitle = "ALTA 2013",

publisher = "Australasian Language Technology Association",

note = "Australasian Language Technology Workshop (11th : 2013) ; Conference date: 04-12-2013 Through 06-12-2013",

}

Ekbal, A, Saha, S, Mollá, D & Ravikumar, K 2013, Multi-objective optimization for clustering of medical publications. in S Karimi & K Verspoor (eds), ALTA 2013: Proceedings of the Australasian Language Technology Association Workshop. Australasian Language Technology Association, Brisbane, pp. 53-61, Australasian Language Technology Workshop (11th : 2013), Brisbane, 4/12/13. <http://aclweb.org/anthology//U/U13/U13-1008.pdf>

Multi-objective optimization for clustering of medical publications. / Ekbal, Asif; Saha, Sriparna; Mollá, Diego et al.
ALTA 2013: Proceedings of the Australasian Language Technology Association Workshop. ed. / Sarvnaz Karimi; Karin Verspoor. Brisbane: Australasian Language Technology Association, 2013. p. 53-61.

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

TY - GEN

T1 - Multi-objective optimization for clustering of medical publications

AU - Ekbal, Asif

AU - Saha, Sriparna

AU - Mollá, Diego

AU - Ravikumar, K.

PY - 2013

Y1 - 2013

N2 - Clustering the results of a search can help a multi-document summarizer present a summary for evidence based medicine (EBM). In this work, we introduce a clustering technique that is based on multiobjective (MOO) optimization. MOO is a technique that shows promise in the areas of machine learning and natural language processing. In our approach we show how MOO based semi-supervised clustering technique can be effectively used for EBM.

AB - Clustering the results of a search can help a multi-document summarizer present a summary for evidence based medicine (EBM). In this work, we introduce a clustering technique that is based on multiobjective (MOO) optimization. MOO is a technique that shows promise in the areas of machine learning and natural language processing. In our approach we show how MOO based semi-supervised clustering technique can be effectively used for EBM.

M3 - Conference proceeding contribution

SP - 53

EP - 61

BT - ALTA 2013

A2 - Karimi, Sarvnaz

A2 - Verspoor, Karin

PB - Australasian Language Technology Association

CY - Brisbane

T2 - Australasian Language Technology Workshop (11th : 2013)

Y2 - 4 December 2013 through 6 December 2013

ER -

Multi-objective optimization for clustering of medical publications

Abstract

Workshop

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Cite this