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A conversational system needs to know how to switch between topics tocontinue the conversation for a more extended period. For this topic detectionfrom dialogue corpus has become an important task for a conversation andaccurate prediction of conversation topics is important for creating coherentand engaging dialogue systems. In this paper, we proposed a topic detectionapproach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering avocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW)technique. In the experiment, we use K-mean clustering with Elbow Method forinterpretation and validation of consistency within-cluster analysis to selectthe optimal number of clusters. We evaluate our approach by comparing it withtraditional LDA and clustering technique. The experimental results show thatcombining PLDA with Elbow method selects the optimal number of clusters andrefine the topics for the conversation.

Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method