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Method for extracting data (gene) characteristic template, and method and system for applying template

A technology of templates and eigenvalues, applied in the fields of intelligent business and intelligent social networking, which can solve the problems of information granularity recognition and grasping deviation, etc.

Active Publication Date: 2017-11-10
刘洪利
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It may be affected by the thinking vision of the initial machine translation application (which is still not successful), people have certain deviations in understanding and grasping the granularity of information in the process of natural language processing

Method used

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  • Method for extracting data (gene) characteristic template, and method and system for applying template
  • Method for extracting data (gene) characteristic template, and method and system for applying template
  • Method for extracting data (gene) characteristic template, and method and system for applying template

Examples

Experimental program
Comparison scheme
Effect test

example

[0464] Example data: ...: teacher: education: male student: like: kung fu: ... = word frequency ratio ...: v: (n+n2): v: ..

[0465]

[0466] Table 16

[0467] Example result data:

[0468] 1. Template A:

[0469] Teacher: Education: Male student: like ≈ word frequency ratio n+n2: v: n+n2: v≈120+300: 2000: 130+150: 10,000: 100+200≈420: 2000: 280: 10000: 300≈ 21:100:14:500:15

[0470] Template B:

[0471] Teacher: Education: Male student: like ≈ word frequency ratio n+n2: v: n+n2: v≈150+350: 1000: 120+130: 9000: 140+170≈500: 1000: 250: 9000: 310≈50 :100:25:900:31

[0472] Template C:

[0473] Teacher: Education: Male student: like ≈ word frequency ratio n+n2: v: n+n2: v≈440+400: 4000: 210+350: 20,000: 400+200≈840: 4000: 560: 20,000: 600 ≈21:100:14:500:15

[0474] Template D:

[0475] Teacher: Education: Male student: like ≈ word frequency ratio n+n2: v: n+n2: v≈360+900: 6000: 390+450: 30,000: 300+600≈1260: 6000: 840: 30,000: 900 ≈21:100:14:500:15

[0476] result: ...

Embodiment

[0495] Example data: comparison of the same noun "male student"

[0496]

[0497] Table 18

[0498] Example result data:

[0499] 1. Template A and Template B do not have the same verb for "male student";

[0500] 2. Template B has 10 times more word frequency than template A "male student";

[0501] 3. The high-frequency verbs related to the noun "male student" in template A are:

[0502] Education (2000) / Like (10,000) Enlightenment (v) / Good at (v)..

[0503] 4. The high-frequency verbs related to the noun "male student" in template B are:

[0504] Exercise (18000) / Learn (140,000) Love (v) / Love (v)...

[0505] The same verb and the same noun, the word frequency ratio is not equal:

[0506] Table 11 Example data: comparison of liking: effort = word frequency ratio v: n2

[0507] Result: Template A and Template B

[0508] The word frequency ratio is 50:1 (1 out of 50 likes is Kung Fu) and 120:1 (1 out of 120 likes is Kung Fu), which shows that "Kung Fu" accounts ...

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Abstract

A method for extracting data association characteristic value mode or template comprises the steps of I, carrying out language determination and preprocessing on a data resource, tagging parts of speech, and tagging nouns and verbs of each sentence, performing grammatical analysis, and tagging a subject, a predicate and an object of each sentence; II, extracting a word group which is repeatedly tagged as the subject and the noun, a word group which is repeatedly tagged as the predicate and the verb and a word group which is repeatedly tagged as the object and the noun in a sentence set, and respectively obtaining a noun set as the subject, a verb set as the predicate and the noun set as the object, and a correlation characteristic relation corresponding to the subject-predicate / predicate-subject in the sentence; and III, respectively counting the accumulative word frequency of the subject nouns, the predicate verbs and the subject nouns, tagging the subject nouns, the predicate verbs and the subject nouns to measure weight characteristic value size of the subject noun set to the predicate verb set / the predicate verb set to the object noun word group with association relation, wherein the ratio of nouns to verbs is approximately equal to the ratio of word frequency n:v, and the ratio of verbs to nouns is approximately equal to the ratio of word frequency v:2n.

Description

technical field [0001] The present invention relates to the fields of data mining, text mining, natural language processing, artificial intelligence, etc., and in particular to a method for making a data-associated feature pattern or template based on natural language processing and text mining, and an intelligent business, Method and system for intelligent social interaction. Background technique [0002] "Data is exploding, but information is very poor." Simply put, data are symbols. The data itself has no meaning, the meaning of the data is semantic (semantic). Only data that is endowed with meaning can be used. At this time, data is transformed into information, and the meaning of data is semantics. Semantics are the means by which computer representations are linked to the real world. [0003] The network resource environment itself is also developing in the direction of semantic, structured and intelligent. [0004] With the development of human beings, a large am...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/24522G06F40/289G06F40/30
Inventor 刘洪利
Owner 刘洪利
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