Abnormal event-oriented relationship extraction method

A technology for extracting abnormal events and relationships, applied in computer parts, biological neural network models, semantic analysis, etc., can solve problems such as inability to cover information extraction well, entity overlap, and reduced extraction efficiency, so as to solve the problem of information extraction. Confusion, improve the efficiency of joint extraction, and increase the effect of utilization

Pending Publication Date: 2020-11-17
GUILIN UNIV OF ELECTRONIC TECH
View PDF9 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The current mainstream information extraction models cannot cover the important features of information extraction well.
If there are more than two entities appearing at the same time, the system model based purely on entity recognition has the problem of low recall rate and poor sampling rate when facing one-to-many samples; The influence of entity overlap cannot be solved for many samples, and the extraction efficiency is also greatly reduced

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
  • Abnormal event-oriented relationship extraction method
  • Abnormal event-oriented relationship extraction method
  • Abnormal event-oriented relationship extraction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0052] This embodiment provides a relation extraction method oriented to abnormal events, such as figure 1 As shown in the method flowchart, taking a fire event as an example, the method includes the following steps:

[0053] S1: Obtain the annotated textual entity-relationship data set of abnormal events, and organize them according to triplets;

[0054] In this embodiment, the present invention organizes the abnormal event text entity relationship data set according to the specific method of triplets as follows:

[0055] Set entity 1 as e1, start position as head e1 , the end position is tail e1 , the entity category is k1, the corresponding entity 2 is e2, head e2 , tail e2 , k2, the relationship is r, n is the total number of entities, rearrange the triples that have the same main entity as entity 1, and the reconstruction form is expressed as:

[0056] {(head e1 , tail e1 ,k 1 ):[(head e1 , tail e1 ,r),(head e2 , tail e2 ,r),...(head en , tail en ,r)]}

[005...

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 an abnormal event-oriented relationship extraction method, which comprises the following steps of: arranging an entity relationship data set related to an emergency according to a structured triad form, and converting statements in related fields into vectorized expressions; constructing a shared coding layer by using a bidirectional long-short-term memory network in combination with a self-attention mechanism; predicting the annotation of the main entity by using a softmax function, performing shared coding on the relational customer entity by using a convolutional neural network, and enhancing coding representation through a prediction result of the main entity; and optimizing the training parameters by using the self-attention mechanism again. A conflict problemof multiple pairs of entities and relationship categories in the emergency text can be well solved, and the extraction quality of the text entity relationship in the field can be improved.

Description

technical field [0001] The invention relates to the field of relation extraction in natural language processing technology, in particular to a relation extraction method oriented to abnormal events. Background technique [0002] With the continuous development of the global economy and the continuous increase of the world population, the number of tourists in the scenic spot is increasing, so the scenic spot has become a highly populated place, making it prone to various abnormal events that affect the order of the scenic spot and even cause serious damage to public property. loss. At present, the research on abnormal events in scenic spots mainly uses monitoring methods to prevent them. However, various abnormal events are complex and changeable. Real-time monitoring can only make corresponding preparations after the event occurs. Provide decision-making assistance for abnormal events in the subsequent processing process. Entity relationship extraction is one of the core ...

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): G06F40/30G06N3/04G06K9/62G06F40/295G06Q10/04
CPCG06F40/30G06F40/295G06Q10/04G06N3/044G06N3/045G06F18/214
Inventor 钟艳如贺昭荣赵蕾先汪先登李芳罗笑南
Owner GUILIN UNIV OF ELECTRONIC TECH
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