Natural Language Interface Models for Fast Responsiveness Applications

A fast responsiveness system incurs minimum latency and produces high throughput and quick response. Interacting with these systems using friendly natural language interfaces requires nat­ ural language (NL) capability and an NL processing component. Examples of fast responsive systems include mission-critical systems like aerospace applications and real-time text messaging applications. The NL component constitutes an important part of the interface. We propose NL interface models for fast responsiveness systems with a language disambiguation component. The language disambiguation component is based on using supervised Machine Learning (ML) techniques. For that, we designed and implemented a number of language disambiguation techniques for word prediction to be used with such systems that require fast responsiveness. In the experimental evaluation, proposed techniques demonstrated impressive performance in prediction accuracy. AFAST RESPONSIVENESS SYS­ tem is a system that incurs minimum latency and pro­ duces high throughput and quick responses. Interacting with such systems requires smart interfaces with capabilities to maintain and support fast responsiveness. The smart interfaces of these systems require natural language (NL) capabilities and a natural language processing (NLP) component. Examples of fast responsiveness systems include Virtual Reality (VR) training programs, real-time Dr. Hisham Al-Mubaid text messaging applications, mission-critical systems like aero­ space applications, and applications in which immediate responses are needed. In general, fast responsiveness is partic­ ularly important for (1) mission critical, (2) real time, and (3) aerospace applications. The NLP component constitutes an important part of the interface, but it is the hard part since working with NL will lead to facing the difficult problem of NL ambiguity. In addition, having an efficient NL component is very appealing for fast responsiveness applications. We present efficient models of interfaces with NL capabilities for fast responsiveness systems. An important part of the interface is the language disambiguation component. The language disambiguation component is based on integrating adaptive and supervised Machine Learning (ML) techniques. ML techniques have demonstrated outstanding suc­ cess in many similar problems. The basic structure of the proUser Interface