Semi-supervised topic classification for low resource languages

In this paper, we present a novel methodology for rapidly developing a topic-based document classification system for a language that has limited resources. Our approach, a hybrid one, combines supervised and unsupervised topic classification techniques. Given that access to native speakers is fairl...

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Veröffentlicht in:	2008 IEEE International Conference on Acoustics, Speech and Signal Processing S. 5093 - 5096
Hauptverfasser:	Daben Liu, McVeety, S., Prasad, R., Natarajan, P.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.03.2008
Schlagworte:	Broadcasting Hidden Markov Model Hidden Markov models Humans Internet Malay Natural languages off-topic rejection Runtime Search engines Testing topic clustering Topology unsupervised topic discovery Web sites
ISBN:	9781424414833, 1424414830
ISSN:	1520-6149
Online-Zugang:	Volltext
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