Enhancing Financial Named Entity Recognition through Adaptive Few-Shot Learning: A Comparative Study of Pre-trained Language Models Article Swipe

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Ziyi Wang ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.69987/jacs.2024.40702

This research article explores the application of machine learning techniques in automated assessment systems within educational contexts. The study investigates how machine learning algorithms can enhance the objectivity and efficiency of educational evaluations, addressing the challenges of traditional assessment methods. Through a comprehensive literature review, analysis of current technologies, and case studies, this research demonstrates the potential of machine learning to revolutionize assessment practices. The findings indicate significant improvements in assessment accuracy, consistency, and time efficiency when utilizing machine learning-based automated systems. However, the study also highlights important considerations regarding ethical implications, potential biases, and the need for human oversight. This research contributes to the growing body of knowledge on educational technology and provides valuable insights for educators, policymakers, and technology developers in the field of educational assessment.

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Type: article
Language: en
Landing Page: https://doi.org/10.69987/jacs.2024.40702
PDF: https://scipublication.com/index.php/JACS/article/download/238/218
OA Status: diamond
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Title: Enhancing Financial Named Entity Recognition through Adaptive Few-Shot Learning: A Comparative Study of Pre-trained Language Models

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Language: en

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Publication year: 2024

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Publication date: 2024-07-08

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Authors: Ziyi Wang

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Landing page: https://doi.org/10.69987/jacs.2024.40702

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PDF URL: https://scipublication.com/index.php/JACS/article/download/238/218

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