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Natural Language Processing

The goal of the Natural Language Processing (NLP) group is to design and build software that will analyze, understand, and generate languages that humans use naturally, so that eventually you will be able to address your computer as though you were addressing another person.

This goal is not easy to reach. “Understanding” language means, among other things, knowing what concepts a word or phrase stands for and knowing how to link those concepts together in a meaningful way. It’s ironic that natural language, the symbol system that is easiest for humans to learn and use, is hardest for a computer to master. Long after machines have proven capable of inverting large matrices with speed and grace, they still fail to master the basics of our spoken and written languages.

The value to our society of being able to communicate with computers in everyday “natural” language cannot be overstated. Imagine asking your computer “Does this candidate have a good record on the environment?” or “When is the next televised National League baseball game?” Or being able to tell your PC “Please format my homework the way my English professor likes it.” Commercial products can already do some of these things, and AI scientists expect many more in the next decade. One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation—enabling scientists, business people and just plain folks to interact easily with people around the world—is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.

In theory, natural-language processing is a very attractive method of human-computer interaction. Early systems such as SHRDLU, working in restricted “blocks worlds” with restricted vocabularies, worked extremely well, leading researchers to excessive optimism, which was soon lost when the systems were extended to more realistic situations with real-world ambiguity and complexity.

Natural-language understanding is sometimes referred to as an AI-complete problem, because natural-language recognition seems to require extensive knowledge about the outside world and the ability to manipulate it. The definition of “understanding” is one of the major problems in natural-language processing.

Retrieved 14 Abr, 2008 – 12:51


abril 14, 2008 - Posted by | Human Language Technologies |

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