使用LDA和HTML标签进行Web内容主题建模
Hamza H M Altarturi1, Muntadher Saadoon2, Nor Badrul Anuar1
1Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|August 7, 2023
概括
一个新的HTML主题模型 (HTM) 在Web内容分析中提高了主题连贯性. 现有的模型在Web数据上表现不佳,但HTM利用HTML结构来更好地发现和理解主题.
科学领域:
- 自然语言处理自然语言处理.
- 数据挖掘 数据挖掘
- 网站内容分析 内容分析
背景情况:
- 主题建模对于分析数字文档至关重要,但传统模型难以理解Web内容的独特结构.
- 现有的主题模型在Web数据上表现明显不佳,导致主题质量低下,并错过了洞察力.
研究的目的:
- 提出一个专门为Web内容数据设计的创新主题模型.
- 解决现有主题模型在HTML文档中发现连贯主题方面的局限性.
主要方法:
- 介绍HTML主题模型 (HTM),它包含HTML标签来解释网页结构.
- 实验性比较HTM与隐性迪里克莱特分配 (LDA) 以及其变体在Web内容数据上的比较.
主要成果:
- 现有的主题模型显示,与传统文档相比,Web数据的性能下降了多达20倍.
- 拟议的HTM模型在主题连贯性方面比LDA模型实现了35%的改进.
结论:
- 专门的主题模型对于有效的网页内容分析至关重要.
- 通过使用HTML结构,HTM在从Web数据中发现连贯主题方面表现出卓越的表现.
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