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使用双向循环神经网络对大学行政服务的意图分类,该神经网络是由开发的开普勒优化算法修改的双向循环神经网络.

Zhen Yang1, Min Lu2, Shitong Huang3

  • 1College of Political Science and Public Administration, Polytechnic University of the Philippines, 1016, Manila, Philippines.

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概括
此摘要是机器生成的。

这项研究引入了一种新的共同自然语言理解 (NLU) 模型,用于对话代理,增强希腊语和英语的大学生支持. 新模型提高了教育AI应用的准确性和效率.

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双向循环神经网络是一种双向循环神经网络.交谈代理人 交谈代理人开发了开普勒优化算法.教育技术的教育技术.意图分类意图分类意图分类意图分类意图分类命名实体认可 命名实体认可自然语言理解 自然语言理解大学的行政服务.

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 教育技术的教育技术

背景情况:

  • 交谈代理正在通过提供即时支持来改变大学与学生之间的互动.
  • 多语种NLU系统面临着意图分类 (IC) 和命名实体识别 (NER) 单独模型的挑战,导致更高的成本和更低的性能.
  • 现有的NLU方法需要大量的计算资源和内存.

研究的目的:

  • 开发一个联合的NLU模型,将IC和NER集成在一起,以提高多语言教育对话代理的效率和准确性.
  • 在复杂的,多语言的大学环境中解决传统的NLU方法的局限性.
  • 通过先进的人工智能增强学生体验并优化大学支持服务.

主要方法:

  • 一个新的联合模型,结合IC和NER,使用修改的双向长短期内存 (BiLSTM) 网络.
  • 使用新开发的开普勒优化 (DKO) 算法优化联合模型.
  • 希腊语和英语语言模式的实施和评估,以帮助大学生.

主要成果:

  • 拟议的联合NLU模型在准确性,精度和回忆方面超过了最先进的模型.
  • 证明了对希腊语和英语的有效资源利用,提高了NLU准确性.
  • 该模型有效地将深度学习与优化技术相结合.

结论:

  • 联合NLU模型显示了教育环境中认知助手的巨大潜力,改善了学生体验和大学支持.
  • 开发的模型为高等教育中的多语言人工智能应用提供了显著的效率和准确性收益.
  • 这项研究有助于通过多式联络和对话式接口开发将人工智能和教育融合在一起.