Improving long-tail relation extraction via adaptive adjustment and causal inference-Reference-Cited by-同舟云学术

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Improving long-tail relation extraction via adaptive adjustment and causal inference

Published:2023-10 Issue: Volume:552 Page:126563
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Tang Jingyao,Li Lishuang,Lu Hongbin,Zhang Beibei,Wu Haiming

Funder

National Natural Science Foundation of China

Dalian Science and Technology Innovation Fund

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference27 articles.

1. Modeling relations and their mentions without labeled text;Riedel,2010

2. Cooperative denoising for distantly supervised relation extraction;Lei,2018

3. Distantly supervised relation extraction using global hierarchy embeddings and local probability constraints;Peng;Knowl.-Based Syst.,2022

4. Neural relation extraction with selective attention over instances;Lin,2016

5. Graph convolution over pruned dependency trees improves relation extraction;Zhang,2018

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. Mitigating biases in long-tailed recognition via semantic-guided feature transfer;Neurocomputing;2024-07

2. Medi: Multi-Expert with Extra Data Introduction for Long-Tailed Classification;2024

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