Sequence labeling model training method, electronic medical record processing method and related device

A technology for sequence labeling and model training, which is applied in electrical digital data processing, neural learning methods, biological neural network models, etc. It can solve the problem of low sequence labeling accuracy and achieve the effect of improving accuracy.

Active Publication Date: 2019-11-19
NEW H3C BIG DATA TECH CO LTD
View PDF8 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiments of the present invention is to provide a sequence labeling model training method, an electronic medical record processing method and related devices to solve the problem of low accuracy in the existing sequence labeling

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
  • Sequence labeling model training method, electronic medical record processing method and related device
  • Sequence labeling model training method, electronic medical record processing method and related device
  • Sequence labeling model training method, electronic medical record processing method and related device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0046]As an implementation, the feature extraction network may include a first convolutional network layer and an attention layer, the first convolutional network layer may include CNN and an improved CNN, and the improved CNN may include, but not limited to, DCNN, IDCNN, DepthwiseConvolution (depth convolution), PointwiseConvolution (pointwise convolution), Group Convolution (group convolution), etc.

[0047] As another implementation, the feature extraction network can also include a first convolutional network layer, an attention layer, and a second convolutional network layer, and both the first convolutional network layer and the second convolutional network layer can include CNN and improved CNN, the improved CNN can include, but not limited to, DCNN, IDCNN, Depthwise Convolution (depth convolution), PointwiseConvolution (pointwise convolution), Group Convolution (group convolution), etc., the following embodiments use the first convolution Both the network layer and the...

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 embodiment of the invention relates to the technical field of natural language processing, and provides a sequence labeling model training method, an electronic medical record processing method and a related device. The method comprises the steps: obtaining a sample sequence and a standard label sequence of the sample sequence; inputting the sample sequence into a pre-established sequence labeling model, and obtaining an initial vector sequence of the sample sequence by utilizing an initial feature network of the sequence labeling model; inputting the initial vector sequence into a featureextraction network of a sequence labeling model, and obtaining a feature sequence by adopting an attention mechanism; inputting the feature sequence into a label prediction network of a sequence labeling model to obtain a training label result of the sample sequence; and based on the training label result and the standard label sequence, performing iterative correction on the sequence labeling model to obtain a trained sequence labeling model. According to the embodiment of the invention, an attention mechanism is introduced to better learn long-distance feature information in the sequence, so that the accuracy of sequence labeling is effectively improved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of natural language processing, and in particular, relate to a sequence labeling model training method, an electronic medical record processing method, and related devices. Background technique [0002] Natural language processing is a science that studies how to realize effective communication between humans and computers using natural language. In natural language processing, the sequence tagging model is a relatively important model, which is widely used in text processing and other related fields, such as word segmentation tagging, part-of-speech tagging, named entity recognition tagging, dependency syntax analysis tagging, time series analysis, etc. [0003] Traditional sequence labeling models mainly include Hidden Markov Model (HMM), Conditional Random Field (CRF), etc. During sequence labeling, traditional models need to manually find features, resulting in the accuracy of seque...

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): G06F17/27G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/241
Inventor 王李鹏
Owner NEW H3C BIG DATA 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