Mechanism entity extraction method, system and device based on multiple training targets

An entity extraction and target technology, applied in neural learning methods, neural architecture, computer components, etc., can solve problems such as poor quality and low efficiency, and achieve the effect of strengthening extraction, improving prediction accuracy, and avoiding error propagation

Active Publication Date: 2020-11-03
PING AN TECH (SHENZHEN) CO LTD
View PDF8 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The present invention provides a method, system, electronic device and computer storage medium for extracting institutional entities based on multiple training objectives, the main purpose of which is to solve the existing institutional entity extraction The problem of low efficiency and poor quality of the method

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
  • Mechanism entity extraction method, system and device based on multiple training targets
  • Mechanism entity extraction method, system and device based on multiple training targets
  • Mechanism entity extraction method, system and device based on multiple training targets

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] In order to illustrate the method for extracting institutional entities based on multiple training targets provided by the present invention, figure 1 It shows the flow of the method for extracting institutional entities based on multiple training objectives provided by the present invention.

[0038] Such as figure 1 As shown, the method for extracting institutional entities based on multiple training targets provided by the present invention includes:

[0039] S110: Obtain a training sample set, and perform named entity labeling on each training sample in the training sample set.

[0040] It should be noted that the sample here is a piece of text information that includes an institutional entity, for example, it can be a paragraph in a job resume, or it can be a piece of text information on a scholar's homepage in the network.

[0041] Specifically, in the process of labeling each training sample in the training sample set, the named entity labeling method used in the...

Embodiment 2

[0110] Corresponding to the above method, the present application also provides a multi-training target-based institutional entity extraction system, which includes:

[0111] A sample labeling unit, configured to obtain a training sample set and perform named entity labeling on each training sample in the training sample set;

[0112] A model training unit, configured to use the labeled training sample set to train a preset named entity model, so that the named entity model can reach a preset accuracy; wherein, the named entity model includes a first trunk road and a second trunk road , the first trunk path is used to extract the first vector feature set of the input text information, the second trunk path is used to extract the second vector feature set of the input text information; and, the second trunk path is also used to extract the input text information according to the The first vector feature set and the second vector feature set perform sequence labeling on the inpu...

Embodiment 3

[0116] The present invention also provides an electronic device 70 . refer to figure 2 As shown, this figure is a schematic structural diagram of a preferred embodiment of the electronic device 70 provided by the present invention.

[0117]In this embodiment, the electronic device 70 may be a server, a smart phone, a tablet computer, a portable computer, a desktop computer, and other terminal devices with computing functions.

[0118] The electronic device 70 includes: a processor 71 and a memory 72 .

[0119] Memory 72 includes at least one type of readable storage media. The at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a memory card, or the like. In some embodiments, the readable storage medium may be an internal storage unit of the electronic device 70 , such as a hard disk of the electronic device 70 . In other embodiments, the readable storage medium can also be an external m...

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 relates to artificial intelligence, and provides an institution entity extraction method based on multiple training targets, which comprises the following steps: obtaining a training sample set, and carrying out named entity labeling on each training sample in the training sample set; training a preset named entity model by using the labeled training sample set so as to enable the named entity model to reach preset precision; performing sequence labeling on the acquired text information to be detected through the named entity model; and extracting related institution entities inthe to-be-detected text information according to the sequence label. The invention also relates to a blockchain technology. The training sample set is stored in the blockchain. According to the technical scheme provided by the invention, the problems of low efficiency and poor quality of an existing mechanism entity extraction method can be effectively solved.

Description

technical field [0001] The present invention relates to the technical field of information extraction, in particular to a method, system, device and storage medium for extracting institutional entities based on multiple training targets. Background technique [0002] At present, many academic scholar databases, such as AMINER and ORCID, provide scholar information to facilitate users to track the research direction and progress of a certain scholar or his team. For example, some expert team projects are deeply involved in the field of medical scientific research, and are committed to building an expert database in the medical field and building a complete expert knowledge map. [0003] However, in the process of building expert knowledge graphs, establishing a network of relationships between experts and institutions is a valuable and difficult task. The reason is that experts are actors with self-selection capabilities, and they will conduct Flow; for example, expert A may...

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): G06F40/30G06F16/36G06K9/62G06N3/04G06N3/08
CPCG06F40/30G06F16/367G06N3/049G06N3/08G06N3/045G06F18/2415
Inventor 柴玲
Owner PING AN TECH (SHENZHEN) 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