A Study on Deep Linguistic Processing with Special Reference to Semantic and Syntactic Levels

Natural Language Processing (NLP) sets a relation between human and computer where the elements of human language, be it spoken or written, are organized so that a computer can perform tasks accordingly based on their interaction. The goal of the Natural Language Processing (NLP) is to design and make software that will help to analyze, understand, and generate languages that humans use naturally, so that in the long run we will be able to address our computer according to our convenience. This goal is not easy to reach because the natural language, the symbol system, that is easiest for humans to learn and use, is hardest for a computer to master and interpret in a meaningful way. Though machines today are capable of inverting large matrix with speed and grace, they still fail to master the basics of our spoken and written languages. The obvious problems arise from the semantic and syntactic ambiguities which in most of the cases becomes difficult to present through a software programme. As an English speaker we can effortlessly understand a sentence like "My mind is flying in joy". But this sentence presents difficulties to a software program that lacks both our knowledge of the world and our experience with linguistic structures. Deep Linguistic Processing, in this connection, is an important area of study to achieve this goal.