Natural Language Processing: An Introduction
Natural Language Processing, or NLP for short, is a relatively new field that studies language as it is used by people rather than as a function of some external, technological device. In short, it is about how language works, but it goes beyond that. It is all about how language is processed by a human being. It has been around for a long time. And yet people still don’t know much about it.
NLP was developed by two people, Drs. John Grindler and Michael Norton. Their work focuses on the study of how we process information in natural language processing. Natural language processing is a broad subfield of artificial intelligence, linguistics, and computer science concerned with how computers and humans process large quantities of natural language data, specifically how to machine learning to analyze and process it. In particular, they are interested in studying how a machine learning system can be made to recognize patterns and make intelligent inferences about the meaning of words. They also want to know if there is an effective way to make the AI software that can do these things.
In order to explore these questions, it’s necessary to understand natural language processing first. The goal of NLP is to take large amounts of unprocessed, “big data” and to analyze it using a large number of tools and programs. In doing so, they hope to develop machines that can process large amounts of big data without being consumed by it.
In 1994, IBM Watson proved that it was possible to solve many business problems by using an artificially intelligent supercomputer. IBM was one of the first companies to use an artificial intelligence system as a part of its marketing campaigns. Though Watson had incredible success, the machine was not without its drawbacks. It failed to take into account cultural differences and the different needs across cultures; Watson was only effective in English speaking countries. Since then, a lot of research has been done into natural language processing.
One of the biggest concerns is the accuracy of the results generated by such systems. Though it may seem simple to ask a machine to complete hundreds or thousands of tasks, the task of actually gathering, analyzing, and communicating the information gathered can be extremely complex. For this reason, a lot of investment funds has been poured into the development of better artificial intelligence tools. One of the most successful attempts at achieving a good match between human analysis and machine training is the International Computer Processing Association (ICPA) project called the Watson Project.
The Watson project was a joint effort between the IBM and Oxford University. Through the project, a huge amount of research was conducted on how best to leverage the power of natural language processing. Once fully developed, the system would be able to handle and complete all kinds of semantic tasks, such as: composing an email; recognizing and understanding spoken and written text; understanding and answering simple questions; and building new knowledge from previously learned information. In addition to all these capabilities, the machine learning system would also be able to provide true artificial intelligence – the ability to decide and act on its own, without the need for human supervision.
Today’s use cases of natural language processing are far more diverse than those posed by Watson. Companies such as Yahoo! and Microsoft use the system to pre-formulate marketing messages and to predict user behavior. Experts in the field of search engine optimization (SEO) use the concept of natural language processing to help their clients automatically present search results to users based on their past search patterns.
A prime example of this is Google’s AdWords system, which uses a feature known as “ad contextual matching” to allow Google AdWords advertisers to target ads based on keywords recently searched for by their customers. These advertisers no longer need to focus on producing content that will appeal only to a large number of users; they can instead target their ads to those users who meet a given set of natural language processing criteria.
The idea of applying artificial intelligence to solve all human problems is still very much in the realm of science fiction. However, many companies are moving forward with research into AI assistants and computer programs that can perform a wide range of routine tasks, including composing articles, completing customer transactions, providing speech analysis and synthesizing natural language. Even though these computer programs may never replace people who actually use and enjoy a human being, they may prove invaluable as a way to make doing business on the internet as enjoyable as possible.