利用词嵌入来增强共发生网络:统计分析
Diego R Amancio1, Jeaneth Machicao2, Laura V C Quispe1
1Institute of Mathematics and Computer Science - USP, Avenida Trabalhador S ao-carlense, no 400, CEP 13566-590, S ao Carlos, SP, Brazil.
PloS one
|July 11, 2025
概括
将虚拟边缘添加到文本网络中可以改善或损害分析. 研究人员发现,网络指标在区分有意义文本和识别语义与语法重点方面的有效性各不相同.
科学领域:
- 计算语言学 计算语言学
- 网络科学 网络科学
- 自然语言处理自然语言处理.
背景情况:
- 最近的研究探讨了词语共发生网络中的虚拟边缘,使用词语嵌入来增强图形表示,特别是在短文本中.
- 语义边缘对传统的共发生网络的影响尚未完全理解.
- 研究基于文本的网络模型的统计属性对于理解文本含义至关重要.
研究的目的:
- 评估网络指标是否能够区分有意义和无意义的文本.
- 为了确定网络指标是否对语法或语义文本方面更敏感.
- 根据文本特征和任务要求,为选择合适的网络指标提供准则.
主要方法:
- 从FastText词嵌入中获得的虚拟边缘丰富单词共发生网络.
- 分析传统和丰富的基于文本的网络模型的统计属性.
- 评估网络指标的信息性,例如平均最短路径,接近中心性和聚类系数.
主要成果:
- 整合虚拟边缘对网络指标有不同的影响;一些提高,另一些降低信息性.
- 平均最短路径和近距离中心性对于具有虚拟边缘的短文本变得更有信息性.
- 随着虚拟边缘的增加,聚类系数的信息性下降.
- 停止词的包含会影响丰富网络的统计属性.
结论:
- 虚拟边缘可以增强文本网络分析,但需要仔细选择网络指标.
- 网络指标对语义与语法信息的敏感性各不相同.
- 结果为优化基于文本长度和特定应用需求的文本网络分析提供了指导.
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