This paper investigates the role of text categorization in streamlining
stopword extraction in natural language processing (NLP), specifically focusing
on nine African languages alongside French. By leveraging the MasakhaNEWS,
African Stopwords Project, and MasakhaPOS datasets, our findings emphasize that
text categorization effectively identifies domain-agnostic stopwords with over
80% detection success rate for most examined languages. Nevertheless,
linguistic variances result in lower detection rates for certain languages.
Interestingly, we find that while over 40% of stopwords are common across news
categories, less than 15% are unique to a single category. Uncommon stopwords
add depth to text but their classification as stopwords depends on context.
Therefore combining statistical and linguistic approaches creates comprehensive
stopword lists, highlighting the value of our hybrid method. This research
enhances NLP for African languages and underscores the importance of text
categorization in stopword extraction.

该研究通过使用 MasakhaNEWS、African Stopwords Project 和 MasakhaPOS 数据集，针对九种非洲语言和法语，探讨了文本分类在自然语言处理（NLP）中简化停用词提取的作用。研究结果显示，文本分类能够有效识别领域无关的停用词，成功检测率超过 80％，但由于语言差异，某些语言的检测率较低。此外，研究还发现，尽管超过 40％的停用词在不同新闻类别中都有，但只有不到 15％的停用词是某个特定类别独有的。不常见的停用词为文本增添了深度，但它们是否被归类为停用词则取决于上下文。因此，将统计和语言方法相结合可以创建全面的停用词列表，凸显了我们的混合方法的价值。这项研究提升了非洲语言的自然语言处理，并强调了文本分类在停用词提取中的重要性。