Densely Connected Graph Attention Network Based on Iterative Path Reasoning for Document-Level Relation Extraction

Hong-ya, Zhang; Huang, Zhen; Li, Zhenzhen; Li, Dongsheng; Liu, Feng

doi:10.1007/978-3-030-75765-6_22

Advances in Knowledge Discovery and Data Mining

2021

DOI: 10.1007/978-3-030-75765-6_22

|View full text |Cite

Densely Connected Graph Attention Network Based on Iterative Path Reasoning for Document-Level Relation Extraction

Zhang Hong-ya

Zhen Huang

Zhenzhen Li

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Survey of Information Extraction Based on Deep Learning

Yang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

As a core task and an important link in the fields of natural language understanding and information retrieval, information extraction (IE) can structure and semanticize unstructured multi-modal information. In recent years, deep learning (DL) has attracted considerable research attention to IE tasks. Deep learning-based entity relation extraction techniques have gradually surpassed traditional feature- and kernel-function-based methods in terms of the depth of feature extraction and model accuracy. In this paper, we explain the basic concepts of IE and DL, primarily expounding on the research progress and achievements of DL technologies in the field of IE. At the level of IE tasks, it is expounded from entity relationship extraction, event extraction, and multi-modal information extraction three aspects, and creates a comparative analysis of various extraction techniques. We also summarize the prospects and development trends in DL in the field of IE as well as difficulties requiring further study. It is believed that research can be carried out in the direction of multi-model and multi-task joint extraction, information extraction based on knowledge enhancement, and information fusion based on multi-modal at the method level. At the model level, further research should be carried out in the aspects of strengthening theoretical research, model lightweight, and improving model generalization ability.

show abstract

A Survey of Information Extraction Based on Deep Learning

Yang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Densely Connected Graph Attention Network Based on Iterative Path Reasoning for Document-Level Relation Extraction

Cited by 1 publication

References 20 publications

A Survey of Information Extraction Based on Deep Learning

A Survey of Information Extraction Based on Deep Learning

Contact Info

Product

Resources

About