Text Categorization Can Enhance Domain-Agnostic Stopword Extraction
Houcemeddine Turki Naome A. Etori Mohamed Ali Hadj Taieb Abdul-Hakeem Omotayo Chris Chinenye Emezue Mohamed Ben Aouicha Ayodele Awokoya Falalu Ibrahim Lawan Doreen Nixdorf
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.