| Summary: | 碩士 === 國立嘉義大學 === 資訊工程學系研究所 === 101 === The topic information of conversational content is important for continuation with communication, so topic detection and tracking is one of important research. Due to there are many topic transform occurring frequently in long time communication, and the conversation maybe have many topics, so it’s important to detect different topics in conversational content. This paper detects topic information by using agglomerative clustering of utterances and Dynamic Latent Dirichlet Allocation topic model, uses proportion of verb and noun to analyze similarity between utterances and cluster all utterances in conversational content by agglomerative clustering algorithm. The topic structure of conversational content is friability, so we use speech act information and gets the hypernym information by E-HowNet that obtains robustness of words. Traditional Latent Dirichlet Allocation topic model detects topic in file units, it just can detect only one topic if uses it in conversational content, because of there are many topics in conversational content frequently, our research considers time sequence of dynamic concept to Latent Dirichlet Allocation topic model training, and also uses speech act information and hypernym information to train the Dynamic Latent Dirichlet Allocation model, then uses Dynamic Latent Dirichlet Allocation model to detect different topic information in long time conversational content.
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