Sense understanding of text conversation using temporal convolution neural network

This paper proposes a model which uses Spatio Temporal features for real-time sense understanding of a text conversation. The proposed model uses CNN along with the concept of LSTM to create a new Spatio temporal cell. Furthermore, the proposed model is used to classify the sentences into eight senses. The model achieved an F-Score around 0.984 on sense classification. Additionally, the efficiency and capabilities of the model are also tested on a standard IMDB sentiment classification dataset. On the IMDB dataset, the model gave an accuracy of 89.27. The experimental results show that the proposed model works better than a CNN model, a Bi-LSTM model, and a combination of CNN & LSTM model in terms of a number of parameters and execution time.