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Semantic risk calculating method based on text logical characteristic

A logic feature and risk calculation technology, applied in calculation, semantic analysis, special data processing applications, etc., can solve problems such as inability to accurately hit objects, lack of model support, and inability to identify risks

Inactive Publication Date: 2016-09-21
BEIJING DEEPDATA TECH
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AI Technical Summary

Problems solved by technology

[0003] Existing semantic risk analysis is mostly filtered based on keywords or keyword databases, lacking model support, and can neither accurately hit objects nor identify risks when faced with complex text or complex semantics. Therefore, under the scale of massive data and text, Unable to achieve efficient and accurate risk mining processing, how to design a semantic risk mining technology that can be used under massive and diverse data sets is very meaningful

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  • Semantic risk calculating method based on text logical characteristic
  • Semantic risk calculating method based on text logical characteristic
  • Semantic risk calculating method based on text logical characteristic

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Embodiment Construction

[0063] The present invention will be further described below in conjunction with accompanying drawings 1 and 2 and embodiments.

[0064] The semantic risk type definition defines different semantic risks as four semantic structures according to semantic logic, including object (A), location (B), behavior (C), feature (D), and semantic structures such as A-D are all phrase sets:

[0065] Set the semantic structure A={a 1 ,...,a n}, where n is an integer greater than or equal to 1, a 1 -a n is a semantic word in semantic structure A;

[0066] Set semantic structure B={b 1 ,...,b n}, where n is an integer greater than or equal to 1, b 1 -b n is a semantic word in semantic structure B;

[0067] Set the semantic structure C = {c 1， ,...,c n}, where n is an integer greater than or equal to 1, c 1 -c n is a semantic word in semantic structure C;

[0068] Set the semantic structure D = {d 1， ,...,d n}, where n is an integer greater than or equal to 1, d 1 -d n is a se...

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Abstract

The invention discloses a semantic risk calculating method based on a text logical characteristic. The semantic risk calculating method comprises the steps of predefining a structure type, and forming a preset-type semantic logic combination; dividing a to-be-processed text into a plurality of texts, and performing word cutting; for the word combination of the to-be-processed paragraph, performing matching according to a predefined preset-type semantic logical combination, performing traversal of the preset-type semantic logical combination, performing paragraph risk value calculation on all word sets with defined semantic structures; performing matching calculation processing on all paragraphs; classifying matching risk sets of all paragraphs of the to-be-processed text, performing accumulation according to the paragraph weight and the paragraph risk value, and obtaining sequential risks and scores of the risks after protocol calculation. The semantic risk calculating method can realize high-efficiency and accurate risk mining.

Description

technical field [0001] The invention relates to the technical fields of Chinese semantic processing and data mining, in particular to a method for automatic identification and processing of semantic risks under large-scale data. Background technique [0002] With the vigorous development of Internet technology, especially the explosive growth of Internet data in recent years, risk identification and control are becoming more and more important. Typical application scenarios include finance, intelligence and other fields. Taking finance as an example, the existing financial risk Control technology is mainly aimed at the analysis of structured data represented by financial data, but how to quickly identify risk information in massive, unstructured Internet data has become an important topic, and it is also a financial institution represented by banks. content of great concern. [0003] Existing semantic risk analysis is mostly filtered based on keywords or keyword databases, ...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06F17/27
CPCG06F40/30G06Q10/0635
Inventor 黄玉麟韩东东林春雨
Owner BEIJING DEEPDATA TECH
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