The Natural language understanding reference article from the English Wikipedia on 24-Apr-2004 (provided by Fixed Reference: snapshots of Wikipedia from wikipedia.org)

Natural language understanding

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:

• The sentences We gave the monkeys the bananas because they were hungry and We gave the monkeys the bananas because they were over-ripe have the same surface grammatical structure. However, in one of them the word they refers to the monkeys, in the other it refers to the bananas: the sentence cannot be parsed properly without knowledge of the properties and behaviour of monkeys and bananas.

• A string of words may be interpreted in a myriad of ways. For example, the string Time flies like an arrow may be interpreted in a variety of ways:
  • time moves quickly just like an arrow does;
  • measure the speed of flying insects like you would measure that of an arrow;
  • measure the speed of flying insects like an arrow would;
  • measure the speed of flying insects that are like arrows;
  • a type of flying insect, "time-flies," enjoy arrows (compare Fruit flies like a banana.)

The word "time" alone can be interpreted as three different parts of speech, (noun in the first example, verb in 2, 3, 4, and adjective in 5).

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

• English and several other languages don't specify which word an adjective applies to. For example, in the string "pretty little girls' school".
  • Does the school look little?
  • Do the girls look little?
  • Do the girls look pretty?
  • Does the school look pretty?

To help this problem, some linguists and artificial intelligence researchers have proposed using an artificial language, that is capable of expressing all the nuance and subtlety of the natural languages we are familiar with, but would have mathematically inviolate grammar and spelling rules, to remove all possible confusion about what a sentence is trying to say, even if it were nonsense words. An example of such a constructed language that could be used for higher order human/computer communication is lojban.

See Also

• linguistics, including Computational linguistics
• machine translation
• speech recognition
• natural language processing

External Links

• 2004 International Workshop on Natural Language Understanding and Cognitive Science

This is the "Natural language understanding" reference article from the English Wikipedia. All text is available under the terms of the GNU Free Documentation License. See also our Disclaimer.