From the perspective of Cognitive Linguistics, grammar is seen as the rules of arrangement of language which best serve communication of the experience of the human organism through its cognitive skills which include perception, attention, motor skills, and visual and spatial processing. Such rules are derived from observing the conventionalized pairings of meaning to understand sub-context in the evolution of language patterns. The cognitive approach to identifying sub-context by observing what comes before and after each linguistic construct provides a grounding of meaning in terms of sensorimotoric embodied experience. When taken together, these two perspectives form the basis of defining approaches in computational linguistics with strategies to work through the symbol grounding problem which posits that, for a computer, a word is merely a symbol, which is a symbol for another symbol and so on in an unending chain without grounding in human experience. The broad set of tools and methods of computational linguistics are available as natural language processing or NLP. Cognitive linguistics adds a new set of capabilities to NLP. These cognitive NLP methods enable software to analyze sub-context in terms of internal embodied experience. Methods The goal of natural language processing (NLP) is to enable a computer to "understand" the contents of text and documents, including the contextual nuances of the language within them. The perspective of Traditional Chomskyan Linguistics offers NLP three approaches or methods to identify and quantify the literal contents, the who, what, where and when in text – in linguistic terms, the semantic meaning or semantics of the text. The perspective of cognitive linguistics offers NLP a direction to identify and quantify the contextual nuances, the why and how in text – in linguistics terms, the implied pragmatic meaning or pragmatics of text. The three NLP approaches to understanding literal semantics in text based on traditional linguistics are symbolic NLP, statistical NLP, and neural NLP. The first method, symbolic NLP (1950s - early 1990s) is based on first principles and rules of traditional linguistics. The second method, statistical NLP (1990s - 2010s), builds upon the first method with a layer of human curated & machine-assisted corpora for multiple contexts. The third approach neural NLP (2010 onwards), builds upon the earlier methods by leveraging advances in deep neural network-style methods to automate tabulation of corpora & parse models for multiple contexts in shorter periods of time. All three methods are used to power NLP