Simplifying Natural Language Processing

NLP is a cross-discipline of logic, philosophy and engineering. NLP is essentially a logic device that accepts inputs from the user and operates on information to produce outputs. Conventional computing databases store chunks of information, utilizing machine learning, NLP correlates data points from user input to unstructured data sets. NLP, machine learning models learn from training, testing, and validation data to build complex matrices in order to produce predictive models based on confidence scores. Leveraging data analytic methods, such as neural nets, Naive Bayes models, and linear regression, NLP allows for a deep understanding of users needs on a contextual, syntactic level. NLP models derive learning from continuous interactions and change their algorithmic, predictive confidence based on new stimuli. An example of general machine learning modeling, in simulated games, tens of thousands of motions are calculated with brute force methods so that the most acceptable move is chosen. When an human plays chess, it is based on experience and abilities, machine learning models and specifically NLP rely on the same human methods of compounded understanding expect at an exponentially rapid rate in milliseconds.

A well known display of machine learning was an experimental game in 1997 between Grandmaster Garry Kasparov, the highest ranked human chess player, and Deep Blue, IBM's super computer. Deep Blue won the series 21 times, with 3 matches being drawn. Deep Blue, with its capability of assessing 200 million positions a second, was the most powerful computer that faced a world chess champion. From the 1990 s, computer language recognition attained a practical level for limited functions with basic conversational engines being developed. During these earlier days of computing, language recognition was considered a stiff challenge, a breakthrough in deep learning has since provided a momentous shift in the abilities of models to produce the NLP technologies today. 

United Airlines developed an applicable use of NLP and machine learning, outside of today's popular voice assistance, by replacing its keyboard tree for flight info with a system using voice recognition with flight numbers and city names. Systems such as these rely on several levels of computation. It requires inputted audio to be translated from verbal words into text before NLP can begin. NLP then involves recognizing the entire text followed by tokenizing the textual values to interpret meaning provided by a comprehension of the text domain, syntax, and in most cases sentiment.

Review: Natural language processing is a place of computational linguistics involved in processing of naturally occurring language. Natural language generation systems convert user input to actionable queries to search unstructured data from databases. This is done by converting samples from human inputted language into more formal representations which are easier for statistical algorithms to manipulate. NLP is still in its infancy and lots of work remains to be done.

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Author: Bryce Jurss