NLP for Intelligent Conversational Assistance

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.