SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks-Reference-Cited by-同舟云学术

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SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks

Published:2022 Issue: Volume:10 Page:60805-60813
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jeong Yuna¹^ORCID,Kim Eunhui¹^ORCID

Affiliation:

1. Korea Institute of Science and Technology Information, Daejoen, Republic of Korea

Funder

Korea Institute of Science and Technology Information

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09791256.pdf?arnumber=9791256

Reference41 articles.

1. What Does BERT Look at? An Analysis of BERT’s Attention

2. How Does BERT Answer Questions?

3. On the stability of fine-tuning BERT: Misconceptions, explanations, and strong baselines;mosbach;Proc 9th Int Conf Learn Represent (ICLR),2021

4. A survey of transformers;lin;arXiv 2106 04554,2021

5. On layer normalization in the transformer architecture;xiong;Proc Int Conf Mach Learn,2020

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1. Multi-Level Attention with 2D Table-Filling for Joint Entity-Relation Extraction;Information;2024-07-14

2. FinDeBERTaV2: Word-Segmentation-Free Pre-trained Language Model for Finance;Transactions of the Japanese Society for Artificial Intelligence;2024-07-01

3. Integration of artificial intelligence and big data in materials science: New paradigms and scientific discoveries;Chinese Science Bulletin;2024-07-01

4. DABC: A Named Entity Recognition Method Incorporating Attention Mechanisms;Mathematics;2024-06-27

5. Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study (Preprint);2023-11-07

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