优化语义或分类模型的窗口大小,以根据上下文和意图识别文本中的引用
Arshad Iqbal1, Abdul Shahid2, Muhammad Roman1
1Institute of Computing Kohat University of Science and Technology, Kohat, Pakistan.
PloS one
|March 24, 2025
概括
识别关键的科学引用需要最佳的词嵌入窗口大小. 更大的窗口,特别是10大小,可以提高引文分析中的机器和深度学习模型的准确性和精度.
科学领域:
- 图书统计学 图书统计学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
背景情况:
- 科学引文对于知识转移至关重要,但其意义各不相同.
- 现有的引用分析方法往往忽视了内部引用上下文和最佳参数选择.
- 词嵌入捕捉语义意义,但当前的技术可能会失去上下文与小,固定的窗口大小.
研究的目的:
- 确定最佳的词嵌入窗口大小,用于分析文本引用.
- 为了比较各种分类模型对引用意义的有效性.
- 提高识别关键科学参考文献的准确性.
主要方法:
- 使用了两个基准数据集,其中包含了大量的文本引用.
- 对Word2Vec嵌入的不同窗口大小进行了比较分析.
- 评估深度学习模型 (CNN,GRU,LSTM) 和机器学习模型 (SVM,决策树,Naive Bayes) 使用精度,回忆,F1得分和准确性.
主要成果:
- 更大的引用窗口大小在捕捉语义上更有效,特别是对于长时间的引用.
- 10个窗口大小在机器学习和深度学习模型中展示了卓越的准确性和精度.
- 词嵌入和分类模型的选择显著影响引用分析性能.
结论:
- 最佳的词嵌入窗口大小对于有效的引用分析至关重要.
- 建议使用10个窗口大小,以更好地识别重要的科学引用.
- 这项研究为基于NLP的引用分析中选择参数提供了方法框架.
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