Unsupervised decomposition of a multi-author document based on naive-Bayesian model

Aldebei, K; He, X; Yang, J

Unsupervised decomposition of a multi-author document based on naive-Bayesian model

Aldebei, K He, X

Yang, J

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Publication Type:: Conference Proceeding
Citation:: ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference, 2015, 2 pp. 501 - 505
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Aldebei, K	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference, 2015, 2 pp. 501 - 505	en_US
dc.identifier.isbn	9781941643730	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41812
dc.description.abstract	© 2015 Association for Computational Linguistics. This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the precision of the clustering assignment and the accuracy of the classification process of our method. Experimental results show that the proposed method outperforms two state-of-the-art methods.	en_US
dc.relation.ispartof	ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference	en_US
dc.title	Unsupervised decomposition of a multi-author document based on naive-Bayesian model	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

© 2015 Association for Computational Linguistics. This paper proposes a new unsupervised method for decomposing a multi-author document into authorial components. We assume that we do not know anything about the document and the authors, except the number of the authors of that document. The key idea is to exploit the difference in the posterior probability of the Naive-Bayesian model to increase the precision of the clustering assignment and the accuracy of the classification process of our method. Experimental results show that the proposed method outperforms two state-of-the-art methods.

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http://hdl.handle.net/10453/41812