Semantic recognition method based on word segmentation

A semantic recognition and word segmentation 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 high recognition rate.

Inactive Publication Date: 2019-06-11
武汉优品楚鼎科技有限公司
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  • 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

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  • Semantic recognition method based on word segmentation
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  • Semantic recognition method based on word segmentation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] Such as figure 1 As shown, a semantic recognition method based on word segmentation, including:

[0033] A semantic recognition method based on word segmentation, comprising:

[0034] Step 101, the input device acquires information data and converts it into text data;

[0035] 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;

[0036] 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 entity Matching with the co...

Embodiment 2

[0040] 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.

[0041] 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.

[0042] The word segmentation method is:

[0043] The word segmentation engine initiates a call to each module in the scene application module;

[0044] S...

Embodiment 3

[0064] 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:

[0065] 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}. ...

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PUM

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Abstract

The invention discloses a semantic recognition method based on word segmentation, and the method comprises the steps: calling each engine by a control and scheduling engine, loading each scene moduleby a word segmentation engine, carrying out word segmentation on a question sentence, and generating a keyword and a matched named entity; calling historical records in a database by a semantic analysis engine, matching the matched named entity with a command word template according to the time weight of the historical records, and extracting the command word template; and the knowledge base retrieval engine extracts related information from the database to present according to the command template with the highest time weight. 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 are telescopic, and expansibility is 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 recommend the problem.

Description

technical field [0001] The invention relates to a semantic recognition method, in particular to a distributed semantic recognition method 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 industry and the compl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27
Inventor 陈斌阮曙东陈平汤超
Owner 武汉优品楚鼎科技有限公司
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