Curating Stopwords in Marathi: A TF-IDF Approach for Improved Text Analysis and Information Retrieval
This work addresses a specific bottleneck in text analysis for Marathi, an incremental contribution to NLP tools for low-resource languages.
The paper tackled the lack of stopwords for the low-resource Marathi language by curating a list of 400 words using TF-IDF and human evaluation on a corpus of 24.8 million sentences, and demonstrated its efficacy in text classification tasks.
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 https://github.com/l3cube-pune/MarathiNLP .