In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in traditional statistical techniques, the need arises for frameworks whereby advanced NLP techniques such as topic modelling may be incorporated within classical methodologies. This paper provides a classical, supervised, statistical learning framework for prediction from text, using topic models as a data reduction method and the topics themselves as predictors, alongside typical statistical tools for predictive modelling. We apply this framework in a Social Sciences context (applied animal behaviour) as well as a Humanities context (narrative analysis) as examples of this framework. The results show that topic regression models perform comparably to their much less efficient equivalents that use individual words as predictors.
翻译:在人文和社会科学中,人们对适用于大型文本公司的信息提取、预测、智能联系和减少维度的方法越来越感兴趣,由于这些领域的方法以传统统计技术为基础,因此有必要建立一些框架,使诸如专题建模等先进的国家实验室技术能够被纳入古典方法,本文件提供了一个典型的、受监督的统计学习框架,用专题模型作为数据减少方法进行预测,专题本身作为预测指标,同时使用典型的统计工具进行预测模型。我们把这一框架用于社会科学(应用动物行为)以及人文学背景(说明分析)作为这一框架的例子。结果显示,专题回归模型与使用单个词作为预测指标的低效率等同模型相比,效果要低得多。