  A conversational system needs to know how to switch between topics to
continue the conversation for a more extended period. For this topic detection
from dialogue corpus has become an important task for a conversation and
accurate prediction of conversation topics is important for creating coherent
and engaging dialogue systems. In this paper, we proposed a topic detection
approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a
vocabulary 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 for
interpretation and validation of consistency within-cluster analysis to select
the optimal number of clusters. We evaluate our approach by comparing it with
traditional LDA and clustering technique. The experimental results show that
combining PLDA with Elbow method selects the optimal number of clusters and
refine the topics for the conversation.
