Modeling Machine Learning Agent for Interaction Conversational System Using Max Entropy Approach in Natural Language Processing

Abstract

There are several service-oriented models, where services are deployed for the user. The user chooses any one of them. During the operational life cycle of these services, there are several issues that occur. User wants an interface for complaint. This paper uses the sentence boundary detection, NER, document categorization, and sentiment extraction methodologies. The natural language processing generates the training model which is a statistical representation of current system knowledge. When a user enters the input then training model extracts the value of a different parameter, these parameters used by call center model for better understanding of user input. The machine learning model used to generate a logical response. When the time exceeds the size of sample, data should be increased and the model understanding also more and more accurate. The accuracy of the model depends on the size of the sample training data. When training data size increases the statistical model for the call center is updated. When the user interacts with the call center agent then call center agent to extract the value of all parameters based on the current statistical model which is based on the sample training data. The paper uses different parameter such as NER, Document category, and sentiment for making a better user interaction. The probability of correct response is increase n time if n parameters are used for response generation. Call center module to take help from sentence detection, NER, document categorization and sentiment training model for extraction the value of the parameter. These parameter value helpful for extracting the NLP Text meaning. The response correctness also increases whenever anyone parameter is extracted correctly. The maximum entropy approach is used for making statistical modeling. The training data are taken from the heterogeneous source.