Chinese integrated entity linking method based on graph model

A technology of Chinese collection and graph model, which is applied in special data processing applications, instruments, electrical digital data processing, etc. It can solve the problems of insufficient knowledge in the knowledge base and low efficiency in constructing entity indication graphs, and achieve the accuracy and accuracy of entity indication graphs. Good and efficient effect

Inactive Publication Date: 2015-12-23
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF3 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is used to solve the defects of insufficient knowledge base and low efficiency of constructing entit...

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
  • Chinese integrated entity linking method based on graph model
  • Chinese integrated entity linking method based on graph model
  • Chinese integrated entity linking method based on graph model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach

[0022] figure 1 It is a flowchart of the integrated entity linking method of the present invention, such as figure 1 As shown, the method of Chinese integrated entity linking based on graph model mainly consists of three parts: candidate entity generation, entity indication graph construction, and integrated entity disambiguation. Concrete implementation scheme is as follows:

[0023] 100. Candidate Entity Generation

[0024] Candidate entity generation is the most basic step of the whole method, such as figure 2 As shown, it mainly includes two parts: entity recognition and candidate entity generation. For the entity recognition in step 201, the present invention performs entity recognition with the help of part-of-speech tagging (nr represents person name, ns represents place name, nt represents organization name, nz represents other proper nouns) of the word segmentation tool ICTCLAS of the Chinese Academy of Sciences. Due to the particularity of the Chinese language, ...

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 present invention discloses a Chinese integrated entity linking method based on a graph model. An ambiguous entity in a text can be mapped into a specific entity in a real world, in order to provide aid for knowledge base expansion, information extraction and search engines. The method mainly comprises three parts of generating a candidate entity, constructing an entity indicator diagram, and disambiguating an integrated entity. For a given text, an entity referent item therein is recognized to obtain the candidate entity. The entity referent item and the candidate entity thereof are regarded as graph nodes to construct an entity referent graph. An in-degree and out-degree algorithm is applied to the entity indicator diagram for implementing disambiguation of multiple ambiguous entities in the text. The present invention does not depend on the knowledge base completely in the establishment of the entity indicator diagram, and also can implement incremental evidence mining to find evidence on an encyclopedia webpage. Dependence path analysis is employed to find the possibly related entity referent item. When the dependence path sizes of two entity referent items are within a set range, the two entity referent items are regarded as the possibly related entity referent items. Further, whether their candidate entities have relations in the real world is determined, so that the efficiency of disambiguation is greatly improved.

Description

technical field [0001] The invention relates to the field of natural language processing (NLP), in particular to entity linking, knowledge base extension, information extraction, question answering system and search engine optimization. Background technique [0002] The traditional Chinese entity linking method compares the context similarity between the entity referent and the candidate entity, and then selects the candidate with the highest similarity as the target object of the link. However, this method has defects. First, it does not take advantage of the semantic correlation between entities in the text, and this correlation can improve the accuracy of disambiguation to a large extent; second, the traditional Chinese entity linking method once Only one ambiguous entity can be disambiguated, the efficiency is low and the method of similarity comparison cannot achieve good results for short text entity links. [0003] Existing integrated entity linking methods consider ...

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
IPC IPC(8): G06F17/30
CPCG06F16/9558
Inventor 刘峤刘瑶秦志光其他发明人请求不公开姓名
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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