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.
More recently, REF has also proposed a method for unsupervised topic modeling to address both topic segmentation and identi-486 fication.
Unsupervised Topic Modelling For Multi-Party Spoken Discourse
International Conference On Computational Linguistics And Annual Meeting Of The Association For Computational Linguistics
Computer Science