Abstract or Full-text in Topic Modeling?

Yasar Tekin, A. Cosar
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Abstract

Topic modeling is a text mining technique used for automatic extraction of topics addressed in document collections. Although there are different topic models proposed by researchers, the most preferred one is Latent Dirichlet Allocation (LDA). Despite such widespread use, uncertainties about LDA have not been fully resolved yet. In this study, the effect of using abstracts or full-text articles on LDA model parameters is investigated. For this purpose, LDA parameters are optimized on abstracts and full-texts of articles published in two different scientific journals and the results obtained are compared with each other.
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主题建模中的抽象还是全文?
主题建模是一种文本挖掘技术,用于自动提取文档集合中所处理的主题。尽管研究者们提出了不同的主题模型,但最受青睐的是潜狄利克雷分配(Latent Dirichlet Allocation, LDA)模型。尽管应用如此广泛,但LDA的不确定性尚未完全解决。在本研究中,研究了摘要或全文文章对LDA模型参数的影响。为此,对发表在两种不同科学期刊上的文章摘要和全文进行LDA参数优化,并对结果进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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