A distributed semantic recognition method and system device for the financial industry

A semantic recognition and distributed technology, applied in the field of semantic recognition, can solve the problems of poor human-computer interaction experience, low recognition rate, and matching failure, and achieve the effects of good human-computer interaction experience, high command recognition rate, and strong reliability.

Pending Publication Date: 2019-06-14
武汉优品楚鼎科技有限公司
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] (1) The search speed is slow. Due to the many scenarios in the financial industry and the complexity of the business, if the keyword-based matching method is used, the number of named entities will be very large. On a scale of one million, to cycle search in such a huge keyword library, Not only is the efficiency poor and consumes computer resources, but also the response speed is slow and the human-computer interaction experience is poor
[0004] (2) The maintenance cost of the keyword thesaurus is high. If the keyword-based matching method is adopted, the named entity library is the key to determining the recognition rate, so a lot of manpower must be spent on the maintenance and update of the named entity library
[0005] (3) The recognition rate is low. Because users generally use voice input, there are inevitably accent problems. For example, "Tongzhou Electronics" is said to be "Tong Zou Electronics", and "Jiulong Electric Power" is said to be " Jiunong Power", and many stocks have aliases, such as "Dawn shares" actually refer to "N Daun", so if the matching method based on keywords is used, the matching will fail
[0006] (4) Unable to understand the user's intentions in combination with the user's context. For example, the user asks "Vanke's market" in the first sentence, and "What about Wanda" in the second sentence. If the keyword-based matching method is used, it is impossible to understand the user. The intention of the second sentence is to ask about Wanda’s market situation
[0007] It can be seen that in the financial field, semantic understanding based on keyword matching cannot effectively solve the problem of semantic recognition in the financial field

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A distributed semantic recognition method and system device for the financial industry
  • A distributed semantic recognition method and system device for the financial industry
  • A distributed semantic recognition method and system device for the financial industry

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] Such as figure 1 As shown, a distributed semantic recognition method for the financial industry, including:

[0066] A distributed semantic recognition method for the financial industry, comprising:

[0067] Step 101, the input device acquires information data and converts it into text data;

[0068] Step 102, the control and scheduling engine calls a word segmentation engine, and the word segmentation engine calls a plurality of scene modules in the scene application module, loads each scene module and the corresponding named entity, and performs word segmentation on the text data, according to the word segmentation Sequentially generate keywords and matching named entities;

[0069] Step 103, the control and scheduling engine invokes a semantic analysis engine, and the semantic analysis engine invokes a plurality of scene modules in the scene application module, loads each scene module and the corresponding command word template, and executes the matching named enti...

Embodiment 2

[0073] Such as figure 2 As shown, the word segmentation engine calls a plurality of scene modules in the scene application module, loads the various scene modules and corresponding named entities, performs word segmentation on the text data, and generates keywords and matching named entities according to the sequence of word segmentation The specific method is as follows, taking "say whether the market of Vanke A is good" as an example.

[0074] The word segmentation engine distributes the text data "Say Vanke A's market is good" to multiple scene modules, and in each scene module, the word segmentation engine executes the same word segmentation method to obtain corresponding named entities in different scenes, and then Summarize the matched named entities generated by the named entities in the word segmentation engine.

[0075] The word segmentation method is:

[0076] The word segmentation engine initiates a call to each module in the scene application module;

[0077] S...

Embodiment 3

[0097] The control and scheduling engine calls a semantic analysis engine, and the semantic analysis engine calls a plurality of scene modules in the scene application module, loads each scene module and the corresponding command word template, and uses the same command word for the matching named entity Templates are matched, and the method for extracting the same command word template as the matched named entity includes:

[0098]As described in Example 2, the word segmentation results include matching named entities generated in the order of word segmentation: ${stock.code}${stock.trend}${base.question}, and the command word template is also the same The above named entity: ${stock.code}${stock.trend}${base.question} is the same type. The command word templates can be but not limited to: ${stock.code}, ${stock.code}, ${base.question}, ${stock.code}${stock.trend}, ${stock .trend}${base.question}, ${stock.code}${base.question}, ${stock.code}${stock.trend}${base.question}.

...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a distributed semantic recognition method for the financial industry, and the method comprises the steps of enabling a control and scheduling engine to call each engine, enabling a word segmentation engine to load each scene module, carrying out the word segmentation of a question, and generating a keyword and a matched named entity; loading each scene module by a semanticanalysis engine, matching the matched named entity with a command word template, and extracting the command word template; analyzing a recommendation engine; using the analysis recommendation engine to calculate the matching degree of the command word template and output the command word template with the highest matching degree; and using a knowledge base retrieval engine to extract the related information from the database to present according to the command template with the highest matching degree. The system is high in stability and reliability, high in recognition rate and good in man-machine interaction experience, all modules in the system can be deployed in a multi-point mode, and the flexibility and the expansibility are high. The question of the user can be intelligently combined into the situation to match the most concerned problem of the user, and the problem that the user wants to know can be mined as much as possible to be recommended.

Description

technical field [0001] The invention relates to a semantic recognition method, in particular to a distributed semantic recognition method and system device applied to the Internet financial industry. Background technique [0002] With the development of information technology, traditional search engines can no longer meet people's needs for information acquisition and retrieval, and intelligent customer service systems based on natural language have emerged as the times require. Internet financial enterprises have a huge user base, complex information, and high real-time requirements. How to quickly and accurately recognize the semantics of natural language input by users is the key to intelligent customer service. Traditional semantic understanding usually uses keyword-based precise or fuzzy matching methods, which have the following problems for large-scale financial data and scenarios: [0003] (1) The search speed is slow. Due to the many scenarios in the financial indu...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06Q40/00
Inventor 陈斌阮曙东陈平汤超
Owner 武汉优品楚鼎科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap