Natural Language Processing

Topics: Semantics, Linguistics, Grammar Pages: 6 (1953 words) Published: October 8, 1999
Natural Language Processing

There have been high hopes for Natural Language Processing. Natural Language Processing, also known simply as NLP, is part of the broader field of Artificial Intelligence, the effort towards making machines think. Computers may appear intelligent as they crunch numbers and process information with blazing speed. In truth, computers are nothing but dumb slaves who only understand on or off and are limited to exact instructions. But since the invention of the computer, scientists have been attempting to make computers not only appear intelligent but be intelligent. A truly intelligent computer would not be limited to rigid computer language commands, but instead be able to process and understand the English language. This is the concept behind Natural Language Processing.

The phases a message would go through during NLP would consist of message, syntax, semantics, pragmatics, and intended meaning. (M. A. Fischer, 1987) Syntax is the grammatical structure. Semantics is the literal meaning. Pragmatics is world knowledge, knowledge of the context, and a model of the sender. When syntax, semantics, and pragmatics are applied, accurate Natural Language Processing will exist.

Alan Turing predicted of NLP in 1950 (Daniel Crevier, 1994, page 9):
"I believe that in about fifty years' time it will be possible to program computers .... to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning."

But in 1950, the current computer technology was limited. Because of these limitations, NLP programs of that day focused on exploiting the strengths the computers did have. For example, a program called SYNTHEX tried to determine the meaning of sentences by looking up each word in its encyclopedia. Another early approach was Noam Chomsky's at MIT. He believed that language could be analyzed without any reference to semantics or pragmatics, just by simply looking at the syntax. Both of these techniques did not work. Scientists realized that their Artificial Intelligence programs did not think like people do and since people are much more intelligent than those programs they decided to make their programs think more closely like a person would. So in the late 1950s, scientists shifted from trying to exploit the capabilities of computers to trying to emulate the human brain. (Daniel Crevier, 1994)

Ross Quillian at Carnegie Mellon wanted to try to program the associative aspects of human memory to create better NLP programs. (Daniel Crevier, 1994) Quillian's idea was to determine the meaning of a word by the words around it. For example, look at these sentences: After the strike, the president sent him away. After the strike, the umpire sent him away. Even though these sentences are the same except for one word, they have very different meaning because of the meaning of the word "strike". Quillian said the meaning of strike should be determined by looking at the subject. In the first sentence, the word "president" makes the word "strike" mean labor dispute. In the second sentence, the word "umpire" makes the word "strike" mean that a batter has swung at a baseball and missed.

In 1958, Joseph Weizenbaum had a different approach to Artificial Intelligence, which he discusses in this quote (Daniel Crevier, 1994, page 133):
"Around 1958, I published my first paper, in the commercial magazine Datamation. I had written a program that could play a game called "five in a row." It's like ticktacktoe, except you need rows of five exes or noughts to win. It's also played on an unbounded board; ordinary coordinate will do. The program used a ridiculously simple strategy with no look ahead, but it could beat anyone who played at the same naive level. Since most people had never played the game before, that included just about everybody. Significantly, the paper was entitled: "How to Make...
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