@article {12421,
	title = {SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations},
	journal = {Association for Computational Linguistics},
	year = {2012},
	month = {2012///},
	abstract = {One of the key tasks for analyzing conversa- tional data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of con- versations, focusing only on the words used. We introduce a hierarchical Bayesian nonpara- metric model, Speaker Identity for Topic Seg- mentation (SITS), that discovers (1) the top- ics used in a conversation, (2) how these top- ics are shared across conversations, (3) when these topics shift, and (4) a person-specific tendency to introduce new topics. We eval- uate against current unsupervised segmenta- tion models to show that including person- specific information improves segmentation performance on meeting corpora and on po- litical debates. Moreover, we provide evidence that SITS captures an individual{\textquoteright}s tendency to introduce new topics in political contexts, via analysis of the 2008 US presidential debates and the television program Crossfire.},
	author = {Nguyen,Viet-An and Jordan Boyd-Graber and Resnik, Philip}
}
