Code comments are vital to source code as they help developers with program comprehension tasks. Written in natural language (usually English), code comments convey a variety of different information, which are grouped into specific categories. In this study, we construct 19 binary machine learning classifiers for code comment categories that belong to three different programming languages. We present a comparison of performance scores for different types of machine learning classifiers and show that the Linear SVC classifier has the highest average F1 score of 0.5474.

翻译：代码注释对源代码至关重要，因为它们有助于开发人员理解程序。代码注释以自然语言（通常是英语）编写，传达多种不同信息，这些信息被归入特定类别。在本研究中，我们构建了19个二元机器学习分类器，用于识别属于三种不同编程语言的代码注释类别。我们比较了不同类型机器学习分类器的性能得分，并表明线性SVC分类器的平均F1得分最高，为0.5474。