Training method, device and electronic equipment for a semantic vector extraction model

A training method and semantic technology, applied in semantic analysis, neural learning method, biological neural network model, etc., can solve the problem of low training effect of semantic vector extraction model

Active Publication Date: 2022-03-11
BEIJING BAIDU NETCOM SCI & TECH CO LTD
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, in the training method of the semantic vector extraction model in the related art, due to the extremely small scale of the sample data of the sensitive text, the training effect of the semantic vector extraction model is extremely low due to the phenomenon of overfitting (Overfitting).

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
  • Training method, device and electronic equipment for a semantic vector extraction model
  • Training method, device and electronic equipment for a semantic vector extraction model
  • Training method, device and electronic equipment for a semantic vector extraction model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The exemplary embodiments of the present disclosure will be described below, including various details of the embodiments of the present disclosure to facilitate understanding, and they should be considered simply exemplary. Accordingly, it will be appreciated by those skilled in the art that various changes and modifications can be made without departing from the scope and spirit of the disclosure. Also, for the sake of clarity and concise, the following description is omitted in the following description.

[0042] The technical field according to the plan of the present disclosure is briefly described below:

[0043] Data Processing, is the acquisition, storage, retrieval, processing, transform, and transmission of data. The basic purpose of data processing is to extract and deliberate data for some specific people, which may be a large amount, may be a messy, difficult to understand, and derived a significant data for some specific people. Data processing is the basic li...

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 disclosure discloses a training method, device and electronic equipment for a semantic vector extraction model, which relate to the field of artificial intelligence, and in particular to the technical fields of deep learning and natural language processing. The specific implementation method is: obtain pre-training text, and based on the pre-training text, train the semantic expression extraction network to generate a pre-semantic expression extraction network; obtain sensitive text samples, and input the sensitive text samples into the pre-semantic Recognize in the expression recognition network to obtain the semantic vector of the sensitive text sample; input the semantic vector into the semantic matching network for training, and adjust the pre-semantic expression extraction network and the semantic expression based on the loss value of each training The matching network is used to generate a semantic vector extraction model, which avoids the over-fitting problem that the semantic vector extraction model is prone to when training with small sample data, and improves the efficiency and reliability of the semantic vector extraction model training process.

Description

Technical field [0001] This disclosure relates to the field of data processing, in particular to deep learning and natural language learning technology. Background technique [0002] With the rise of deep learning (Deep Learning, DL)-related technologies, the well-trained semantic vector extraction model is applied to a variety of different application scenarios, which can have better results. In particular, the application scenario for text-sensitive information is approved, and the semantic vector extraction model of performance can promote the spread of harmonious information and purify the Internet environment. [0003] However, the language of the semantic vector extraction model in the related art is extremely low due to the small sample data of sensitive text. It is necessary to cause the semantic vector extraction model due to the ultraptting phenomenon. Therefore, how to improve the efficiency and reliability of the training process of the semantic vector extraction mode...

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 Patents(China)
IPC IPC(8): G06F40/30G06F40/216G06K9/62G06N3/04G06N3/08
CPCG06F40/30G06F40/216G06N3/08G06V10/22G06N3/045G06F18/22G06F18/214
Inventor 杨茵淇
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD
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