Hybrid Data Augmentation for Citation Function Classification-Reference-Cited by-同舟云学术

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Hybrid Data Augmentation for Citation Function Classification

Published:2023-06-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Zhang Yang¹,Wang Yufei¹,Sheng Quan Z.¹,Mahmood Adnan¹,Zhang Wei Emma²,Zhao Rongying³

Affiliation:

1. School of Computing, Macquarie University,Faculty of Science and Engineering,Sydney,Australia

2. School of Computer and Mathematical Sciences, The University of Adelaide,Adelaide,Australia

3. School of Information Management, Wuhan University,Wuhan,China

Funder

Australian Research Council (ARC) Discovery

National Social Science Fund Major Project of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191695.pdf?arnumber=10191695

Reference32 articles.

1. KnowDA: All-in-one knowledge mixture model for data augmentation in low-resource NLP;wang;Proceedings of ICLR,0

2. PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks

3. An annotation scheme for citation function

4. The next decade in ai: four steps towards robust artificial intelligence;marcus;ArXiv Preprint,2020

5. Conditional bert contextual augmentation;wu;Proceedings of ICCS,0

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