Significance of Natural Language Processing in Data Analysis Using Business Intelligence

Introduction

Natural Language Processing is being referred as the AI based technology which qualifies the machines/computers in understanding, interpretating and manipulating the human natural language. It enables the computer systems in reading the characters in text, speech recognition and interpretating it. It is derived from a variety of disciplines like computer science and linguistics computation and tries to remove the gap between communications with computer and with humans. The terminologies involved with NLP are given in Figure 1.

Figure 1.

NLP terminologies

Ref: https://www.blumeglobal.com/learning/natural-language-processing/

Chronicle Of Natural Language Processing

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  Georgetown, 1954 –-An Experiment from IBM – A programmed computer was designed that translates 60 statements from Russian into English by assigning specific rules and steps to specific words.

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  LISP, 1958 -- John McCarthy released the Locator/Identifier Separation Protocol, one of the computer programming language still being used in the current world.

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  STUDENT, 1964 – Daniel Bobrow, as part of his Ph.D. thesis work, developed an program based on AI named STUDENT created in specific to read and resolve word problems on algebra.

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  ELIZA, 1964– A program developed at MIT, which deemed to be first of kind Chatbots, creates the simulation of conversations using the substitution methods and pattern-match methodology.

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  ALPAC, 1964 – NRC, United States National Research Council developed ALPAC (Automatic Language Processing Advisory Committee) to evaluate the development of research work on Natural Language Processing.

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  SHRDLU, 1970 – Terry Winograd created a computer strategy named SHRDLU, which can understand NLP and considered to be the first of kind program that can understand the context, in which the user provides several programming directions to shift several blocks in disparate ways.

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  LADDER/ LIFER, 1978 – An NLP database system which utilized a connotative syntax to analyse questions and inquiry any distribute database to provide solution questions regarding the US Naval ships.

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  MACHINE LEARNING, 1980 – Machine Learning algorithms gathered significance by replacing the traditional complex and handwritten rules used in NLP systems. Few of the ML algorithms like Decision Trees provided the systems with traditional rules which were handwritten but many research activities arose which pivot on statistics based models which are capable enough to produce probability based decisions of which IBM was the major contributor in developing many complicated and successful models with statistics..

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  N-Grams, 1990 – Statistics based NLP model to recognize and track linguistic data in a numerical way.

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  LSTM/RNN, 1997 -- Recurrent Neural n Network models entered in their way for text and voice processing.

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  AskJeeves, 1997 –Search engine evokes users to raise queries with their natural languages.

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  Watson, 2006 – A software combination of Artificial Intelligence and Analytics, was created by IBM to address the queries on hazard on a typical question-answering model of machine.

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  Hummingbird, 2013 –Google product which updated its query mechanism to anchor on the intention of the searcher rather than merely understanding the query type of the search.

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  2011 – Till Date – Alexa, Google Assistant and Siri have become the Virtual assistants like Siri, Alexa, and Google Assistant become the most quality tool out of NLP, being used in almost all smart gadgets.