在阅读理解中使用NLP和KNN自动生成简短答案问题
Lala Septem Riza1, Yahya Firdaus1, Rosa Ariani Sukamto1
1Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia.
概括
本研究介绍了一种自动化方法,用于使用自然语言处理 (NLP) 和K-最近邻居 (KNN) 来生成阅读理解问题. 该系统平均得分为63.23%,为创建问题提供了有效的替代方案.
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:
- 自动化问题生成可以大大减少创建评估所需的时间和精力.
- 手动开发有效的阅读理解问题是耗时的.
- 现有的方法可能在生成不同类型的问题时缺乏效率或准确性.
研究的目的:
- 开发和评估一个系统,自动生成短答题,用于阅读理解.
- 为了利用自然语言处理 (NLP) 和K-最近邻居 (KNN) 来生成问题.
- 根据语法正确性,答案存在和难度来评估生成问题的质量.
主要方法:
- 利用NLP技术进行简单的句子提取和问题分类.
- 雇佣K-最近邻居 (KNN) 算法用于问题句子生成.
- 整合机器学习通过对现有问题的培训来确保质量.
- 使用参数评估生成的问题:语法正确性,答案存在和难度指数.
主要成果:
- 在语法正确性方面获得了59.52%.
- 达到了95.24%的答案存在.
- 在难度指数中获得34.92%.
- 在质量问题上,总体平均得分为63.23%.
结论:
- 开发的系统显示了作为自动化阅读理解问题生成的替代工具的潜力.
- NLP和KNN的结合显示出在创造相关和可回答的问题方面具有前景.
- 需要进一步改进,以提高语法正确性和难度水平的准确性.
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