Stopwords are commonly used words in a language that are often considered to
be of little value in determining the meaning or significance of a document.
These words occur frequently in most texts and don't provide much useful
information for tasks like sentiment analysis and text classification. English,
which is a high-resource language, takes advantage of the availability of
stopwords, whereas low-resource Indian languages like Marathi are very limited,
standardized, and can be used in available packages, but the number of
available words in those packages is low. Our work targets the curation of
stopwords in the Marathi language using the MahaCorpus, with 24.8 million
sentences. We make use of the TF-IDF approach coupled with human evaluation to
curate a strong stopword list of 400 words. We apply the stop word removal to
the text classification task and show its efficacy. The work also presents a
simple recipe for stopword curation in a low-resource language. The stopwords
are integrated into the mahaNLP library and publicly available on
this https URL .

我们研究了停用词在马拉地语中的应用，使用 MahaCorpus 数据集中的 2400 万个句子，结合 TF-IDF 方法和人工评估，筛选出具有强大效果的 400 个停用词，并将其应用于文本分类任务。该研究还提出了在资源有限的语言中进行停用词筛选的简单方法。停用词已集成到 mahaNLP 库中，并在此 https URL 上公开提供。