使用引用网络来评估文本长度对关键词提取的影响
Jorge A V Tohalino1, Thiago C Silva2, Diego R Amancio1
1Institute of Mathematics and Computer Science - USP, São Carlos, SP, Brazil.
PloS one
|November 27, 2023
概括
从完整的论文中提取关键词,与摘要相比,其准确性较低. 文本聚类方法比基于引用的方法表现更好,但结合方法可能会改善关键概念识别的结果.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
背景情况:
- 从非结构化数据中识别关键概念至关重要.
- 有限的研究探讨了文本长度对关键词提取 (KE) 性能的影响.
- 研究经常使用摘要/标题,质疑全文的有效性.
研究的目的:
- 评估摘要和完整论文之间的关键词提取一致性.
- 评估KE的基于网络和文本聚类方法.
- 调查文本长度对KE性能的影响.
主要方法:
- 基于网络的方法,使用引用网络上的社区检测.
- 文本聚类以识别突出的术语 (tf-idf-like).
- 从集群级别的术语中选择的关键词也存在于摘要中.
主要成果:
- 欧盟的KE检测方法也显示出类似的准确性.
- 文本聚类方法优于基于引用的方法.
- 所有测试方法的准确性都相对较低.
- 抽象和全文关键字之间显著缺乏一致性.
结论:
- 从全文中提取关键字不如从摘要中提取的一致性要低.
- 文本聚类和引用分析产生不同的结果.
- 结合方法的混合方法可以提高KE的性能.
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