Evidence Reasoning and Curriculum Learning for Document-level Relation Extraction-Reference-Cited by-同舟云学术

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Evidence Reasoning and Curriculum Learning for Document-level Relation Extraction

Published:2023 Issue: Volume: Page:1-14
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Xu Tianyu¹^ORCID,Qu Jianfeng¹,Hua Wen²^ORCID,Li Zhixu³^ORCID,Xu Jiajie¹^ORCID,Liu An¹^ORCID,Zhao Lei¹^ORCID,Zhou Xiaofang⁴^ORCID

Affiliation:

1. School of Computer Science and Technology, Soochow University, Suzhou, China

2. Department of Computing, Hong Kong Polytechnic University, HongKong, China

3. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, HongKong, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/4358933/10174655.pdf?arnumber=10174655

Reference48 articles.

1. UHop: An unrestricted-hop relation extraction framework for knowledge-based question answering;Chen

2. Neural Knowledge Acquisition via Mutual Attention Between Knowledge Graph and Text

3. Matching the Blanks: Distributional Similarity for Relation Learning

4. Learning from Context or Names? An Empirical Study on Neural Relation Extraction

5. A Frustratingly Easy Approach for Entity and Relation Extraction

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Coarse-to-Fine Framework for Entity-Relation Joint Extraction;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. Accurate graph classification via two-staged contrastive curriculum learning;PLOS ONE;2024-01-03

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