In Computing, What is a Natural Language?

Natural language processing (N LP, Natural Language Processing) is a technology that uses natural language to communicate with computers.Because the key to processing natural language is to let the computer "understand" natural language, natural language processing is also called natural language understanding (NLU, Natural Language Understanding), also known as Computational Linguistics. On the one hand, it is a branch of language information processing; on the other hand, it is one of the core topics of artificial intelligence (AI).

Natural Language Understanding

Language is the essential characteristic of human beings to distinguish other animals. Of all living things, only humans have the ability to speak. Many human intelligences are closely related to language. Human logical thinking is in the form of language, and most of human knowledge is also recorded and passed down in the form of language. Therefore, it is also an important or even core part of artificial intelligence.

Communicating with computers using natural language has long been sought after. Because it has both obvious practical significance and important theoretical significance: people can use computers in the language they are most accustomed to, without having to spend a lot of time and energy on learning various computer languages that are not very natural and customary; People can also use it to further understand human language capabilities and intelligent mechanisms.

Achieving natural language communication between humans and computers means that computers must be able to both understand the meaning of natural language texts and express given intents, ideas, etc. in natural language texts. The former is called natural language understanding and the latter is called

Natural language understanding has been studied since the early 1960s. Due to N. Chomsky's breakthroughs in linguistic theory and the development of various theories since then, as well as the continuous improvement of computer functions, certain results have been achieved at present. For both speech and written comprehension. Speech comprehension uses spoken input to make the computer "understand" speech signals, using text or

From the 1960s to the early 1970s, research had been focused on words.

Starting around the 1990s,

There are two problems at this stage. On the one hand, the grammar so far has been limited to the analysis of an isolated sentence, and the constraints and effects of the context and the speaking environment on this sentence have not been systematically studied. Therefore, the analysis of ambiguity, word omission, and pronoun There are no clear rules for referring to the different meanings of the same sentence on different occasions or by different people, and it needs to be strengthened by pragmatic research to solve it gradually. On the other hand, people do not understand a sentence based on grammar alone. They also use a lot of relevant knowledge, including life knowledge and specialized knowledge, which cannot be stored in a computer. Therefore, a written understanding system can only be established within a limited range of vocabulary, sentence patterns, and specific topics; after the computer's storage capacity and operating speed have greatly increased, it is possible to properly expand the scope.

The above problems have become a major problem in the application of natural language understanding in machine translation, which is one of the reasons why the quality of today's machine translation systems is far from the ideal target; and the quality of translation is the key to the success of machine translation systems. Chinese mathematician and linguist

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