Unsupervised topic modelling for multi-party spoken discourse

Unsupervised topic modelling for multi-party spoken discourse (2006)

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by Matthew Purver , Konrad P. Körding , Thomas L. Griffiths

Venue:	In COLING-ACL 2006
Citations:	42 - 8 self

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Datum	Value	Source
TITLE	Unsupervised topic modelling for multi-party spoken discourse	INFERENCE
AUTHOR NAME	Matthew Purver	SVM HeaderParse 0.2
AUTHOR AFFIL	CSLI; Stanford University	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305, USA	SVM HeaderParse 0.2
AUTHOR NAME	Konrad P. Körding	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Brain & Cognitive Sciences; Massachusetts Institute of Technology	SVM HeaderParse 0.2
AUTHOR ADDR	Cambridge, MA 02139, USA	SVM HeaderParse 0.2
AUTHOR NAME	Thomas L. Griffiths	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Cognitive & Linguistic Sciences; Brown University	SVM HeaderParse 0.2
AUTHOR ADDR	Providence, RI 02912, USA	SVM HeaderParse 0.2
ABSTRACT	We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-party discourse transcripts. We show how Bayesian inference in this generative model can be used to simultaneously address the problems of topic segmentation and topic identification: automatically segmenting multi-party meetings into topically coherent segments with performance which compares well with previous unsupervised segmentation-only methods (Galley et al., 2003) while simultaneously extracting topics which rate highly when assessed for coherence by human judges. We also show that this method appears robust in the face of off-topic dialogue and speech recognition errors. 1	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In COLING-ACL 2006	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	17--24	INFERENCE
CITATIONS	19 found	ParsCit 1.0