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Natural language processing method, device and system based on semantic recognition

A natural language processing and language technology, applied in the field of natural language processing and/or search, which can solve problems such as incomplete search, low search quality, and inaccurate search

Active Publication Date: 2017-11-17
陈伯妤
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, the traditional full-text search technology is based on keyword matching, which often has the phenomenon of incomplete search, inaccurate search, and low search quality.
Especially in the age of network information, it is difficult to use keyword matching to meet people's search requirements.
For example, if a user enters the keyword "apple", does the user refer to fruit or a well-known computer brand? Based on traditional keyword matching retrieval technology, it is impossible to accurately distinguish, so that it is impossible to efficiently and accurately feed back to the user the most needed information. Information

Method used

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  • Natural language processing method, device and system based on semantic recognition
  • Natural language processing method, device and system based on semantic recognition
  • Natural language processing method, device and system based on semantic recognition

Examples

Experimental program
Comparison scheme
Effect test

example 2

[0083] By analyzing the above two examples, it can be found that:

[0084] "Rural tourism", "Chinese tourism" and "tourism development" are equivalent to the variables in Example 1, because by replacing the corresponding parts (ie variables), their semantic meaning can basically be preserved. And "x is an important part of x and an important support to promote x" (where x represents blank) is equivalent to the linear structure of Example 1, which is the constant of language, because the meaning of language is hidden in this linear structure.

[0085] Similarly, "China's economy", "world economy", and "global financial stability" are equivalent to the variables in Example 2, because by replacing the corresponding parts (ie variables), their semantics can basically be preserved. And "x is an important component of x and an important support to promote x" (where x represents blank) is equivalent to the linear structure of Example 2, which is the constant of language, because the ...

example 4

[0092] By analyzing the above two examples, it can be found that "Avatar" and "China" are equivalent to the variables in Example 3, because by replacing the corresponding parts (ie variables), their semantic meaning can basically be preserved. And "x craze sweeps x". (where x represents blank) is equivalent to the linear structure of example 3, which is the constant of language, because the meaning of language is hidden in this linear structure.

[0093] Similarly, "stock trading" and "world" are equivalent to the variables in Example 4, because by replacing the corresponding parts (ie variables), their semantics can basically be preserved. And "x boom sweeps x" (where x represents blank) is equivalent to the linear structure of example 4, which is the constant of language, because the meaning of language is hidden in this linear structure.

[0094] It can be found that the linear structure of the two examples is the same, the difference is only in the variables. "X craze swe...

example 8

[0100] By analyzing the above four examples, it can be found that:

[0101] "They", "European Commission" and "Application for Market Economy Treatment of Chinese Enterprises" are equivalent to the variables in Example 5, because by replacing the corresponding parts (namely variables), their semantic meaning can basically be preserved. And "x appeals to x to treat x objectively and fairly" (where x represents blank) is equivalent to the linear structure of example 5, which is a constant of language, because the meaning of language is hidden in this linear structure.

[0102] Similarly, "FIFA", "Ireland" and "the result of the World Cup qualifier against France" are equivalent to the variables in Example 6, because by replacing the corresponding parts (ie variables), their semantics can basically be preserved. And "x appeals to x to treat x objectively and fairly" (where x represents blank) is equivalent to the linear structure of example 6, which is a constant of language, bec...

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Abstract

A method, device and system for natural language processing on the basis of semantic recognition, comprising: using punctuation to parse words in a text into character strings, and extracting language linear structures and language chunks from said strings (101); listing in reverse order the extracted language linear structures and language chunks (102); creating a language linear structure subindex and a language chunk subindex, and then combining both subindices into a single complete index (103); extracting language linear structures and language chunks from search strings input by a user, and feeding back to the user information matching same (104). The present invention uses a technical means of analyzing language structures + key words to grasp precisely the true meaning of the information therein, and accurately provides the user with the information needed.

Description

technical field [0001] The present invention relates to the field of natural language processing and / or search. More specifically, it relates to a natural language processing method, device and system based on semantic recognition. Background technique [0002] Natural language processing (Information Retrieval) refers to the process and technology of organizing information in a certain way and finding relevant information according to the needs of information users. Natural language processing in a narrow sense is the second half of the natural language processing process, that is, the process of finding the required information from the information collection, which is what we often call information search (Information Search or Information Seek). [0003] Currently commonly used natural language processing methods usually include: common method, retrospective method and segmentation method. The common law is a method of searching literature and materials by using retrie...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/30
Inventor 不公告发明人
Owner 陈伯妤