Text Categorization Can Enhance Domain-Agnostic Stopword Extraction
Abstract
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.
Cite
@article{arxiv.2401.13398,
title = {Text Categorization Can Enhance Domain-Agnostic Stopword Extraction},
author = {Houcemeddine Turki and Naome A. Etori and Mohamed Ali Hadj Taieb and Abdul-Hakeem Omotayo and Chris Chinenye Emezue and Mohamed Ben Aouicha and Ayodele Awokoya and Falalu Ibrahim Lawan and Doreen Nixdorf},
journal= {arXiv preprint arXiv:2401.13398},
year = {2024}
}
Comments
A Project Report for the Masakhane Research Community