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Statistical Methods in the Problem of Studying Apology Speech Formulas and Their Satellites in the English Language

机译:在英语语言中学习道歉语音公式及其卫星问题的统计方法

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This article describes the algorithm of the analysis of justification speech formulas in the English language. We also consider methods of preprocessing text data (normalization, part-of-speech tagging, functional tagging). Such methods as frequency analysis and Apriori (an algorithm for frequent item set mining and association rule learning) have been used for texts analyzing. Based on the statistical analysis results, 11 general justification formulas have been derived. These formulas have also been used to compose an attribute vector for the classifier. The support vector machine classifier (SVM) is used for texts classifying. After training the classifier, 12 000 texts of the language corpus have been analyzed.
机译:本文介绍了英语语义语义语音公式分析的算法。我们还考虑预处理文本数据的方法(归一化,语音标记,功能标记)。这种方法作为频率分析和APRiori(频繁项目集挖掘和关联规则学习的算法)已被用于文本分析。基于统计分析结果,推出了11个一般性公正公式。这些公式也已用于构思分类器的属性向量。支持向量机分类器(SVM)用于分类文本。在培训分类器后,已经分析了12 000个语言语料库的文本。

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