tailieunhanh - Báo cáo khoa học: "SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations"

One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However, most models of topic segmentation ignore the social aspect of conversations, focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model, Speaker Identity for Topic Segmentation (SITS), that discovers (1) the topics used in a conversation, (2) how these topics are shared across conversations, (3) when these topics shift, and (4) a person-specific tendency to introduce new topics. . | SITS A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations Viet-An Nguyen Department of Computer Science and UMIACS University of Maryland College Park MD vietan@ Jordan Boyd-Graber iSchool and UMIACS University of Maryland College Park MD jbg@ Philip Resnik Department of Linguistics and UMIACS University of Maryland College Park MD resnik@ Abstract One of the key tasks for analyzing conversational data is segmenting it into coherent topic segments. However most models of topic segmentation ignore the social aspect of conversations focusing only on the words used. We introduce a hierarchical Bayesian nonparametric model Speaker Identity for Topic Segmentation SITS that discovers 1 the topics used in a conversation 2 how these topics are shared across conversations 3 when these topics shift and 4 a person-specific tendency to introduce new topics. We evaluate against current unsupervised segmentation models to show that including personspecific information improves segmentation performance on meeting corpora and on political debates. Moreover we provide evidence that SITS captures an individual s tendency to introduce new topics in political contexts via analysis of the 2008 US presidential debates and the television program Crossfire. 1 Topic Segmentation as a Social Process Conversation interactive discussion between two or more people is one of the most essential and common forms of communication. Whether in an informal situation or in more formal settings such as a political debate or business meeting a conversation is often not about just one thing topics evolve and are replaced as the conversation unfolds. Discovering this hidden structure in conversations is a key problem for conversational assistants Tur et al. 2010 and tools that summarize Murray et al. 2005 and display Ehlen et al. 2007 conversational data. Topic segmentation also can illuminate individuals agendas .