论文标题
使用机器学习模型对文本进行分类并确定对话漂移
Classifying text using machine learning models and determining conversation drift
论文作者
论文摘要
文本分类通过将单词与此层次结构映射，有助于分析文本的语义含义和相关性。对各种文本的分析对于理解其语义含义及其相关性是无价的。文本分类是对文档进行分类的一种方法。它结合了计算机文本分类和自然语言处理，以总体分析文本。此方法提供了文本的描述性分类，具有内容类型，对象字段，词汇特征和样式特征等功能。在这项研究中，作者旨在在机器学习中使用自然语言特征提取方法，然后将其用于训练一些基本的机器学习模型，例如天真的贝叶斯，逻辑回归和支持向量机。这些模型用于检测教师何时必须参与讨论，当时界限偏离主题。
Text classification helps analyse texts for semantic meaning and relevance, by mapping the words against this hierarchy. An analysis of various types of texts is invaluable to understanding both their semantic meaning, as well as their relevance. Text classification is a method of categorising documents. It combines computer text classification and natural language processing to analyse text in aggregate. This method provides a descriptive categorization of the text, with features like content type, object field, lexical characteristics, and style traits. In this research, the authors aim to use natural language feature extraction methods in machine learning which are then used to train some of the basic machine learning models like Naive Bayes, Logistic Regression, and Support Vector Machine. These models are used to detect when a teacher must get involved in a discussion when the lines go off-topic.
