科学文档处理:现代学习方法的挑战
Abhinav Ramesh Kashyap1,2, Yajing Yang2, Min-Yen Kan2
1ASUS Intelligent Cloud Services (AICS), Singapore, Singapore.
International journal on digital libraries
|June 26, 2023
概括
神经网络由于其结构和多式联络性质,与科学论文作斗争. 本调查探讨了深度学习方法,以解决学术文件处理 (SDP) 的这些挑战.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 神经网络在Web文档任务中表现出色,但在科学出版物方面面临挑战.
- 科学文件具有独特的特点:复杂的话语结构,相互联系和多模式性.
- 现有的方法往往无法充分解决学术文件处理 (SDP) 中的这些具体挑战.
研究的目的:
- 调查用于学术文件处理 (SDP) 的现代神经网络学习方法.
- 确定可以在科学文件中建模话语结构,互联性和多模式的方法.
- 要突出数据集和工具的进步,以深度学习在SDP.
主要方法:
- 审查当前的神经网络架构和学习技术.
- 分析处理话语结构,文档相互连接和多式联络数据的方法.
- 检查大规模数据集的创建和在SDP中深度学习的工具开发.
主要成果:
- 识别能够处理科学文档复杂性的神经网络方法.
- 展示利用话语结构,相互连接和多模式性的方法.
- 对促进SDP深度学习部署的资源的概述.
结论:
- 深度学习为推进学术文件处理 (SDP) 提供了有希望的途径.
- 未来的研究应该专注于提炼话语,互联和多模式的模型.
- 持续开发数据集和工具对于SDP中有效的神经NLP至关重要.
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