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Natural language understanding

From Wikipedia, the free encyclopedia.

Natural language understanding is a sub-field of artificial intelligence research devoted to making computers "understand" statements written in human languages.

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.

Some examples of the problems faced by natural language understanding systems:

English is particularly bad in this regard because it has little inflectional morphology to distinguish between parts of speech.

See also:

External links:


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