NLP for Intelligent Conversational Assistance

Abstract

 Context-specific signals were often used  as extra supportive measures secondary kinds of evidence  to aid interpret its user's language inputs in the early days  of Natural Languages Processing (NLP). The context was  employed in conversational bargaining more as tie breaking technique than as a fundamental components.  Recent advances in the context based reasoning have  prompted paradigm shift away from context-assisted  approaches and toward context centric natural language  processing system. To support today's advanced  Humans Computer Interactions (HCI) application,  including personal digital assistants, languages tutors, as  well as questions answering system, the importance of  context in NLP must evolve. There is indeed a strong  feeling of utilitarian, intent communication in these apps.  The underlying NLP approaches must be capable of  navigating throughout a concept as well as contextual  discussion with such a focus on goal-oriented behavior.  The natural relationship between NLP as well as context based approaches are explored in this paper, as it shows  itself in the frame of reference paradigm. The major goal  of this study is to understand more about NPL technology  for conversational intelligence. Along the process,  insights or examples are presented to shed light on this  evolving approach to natural language-based HCI  architecture. Natural language processing will be able to  leverage its potential for human-like speech or text  interpretation in the future through a variety of applications  thanks to semantic and cognitive technology.