Network-based Approach for Stopwords Detection

Stopword lists, an essential resource for natural language processing and information retrieval, are often unavailable for low-resource languages. Creating these lists is time-consuming and expensive, making automated stopword detection a viable alternative. This paper introduces a novel stopword detection approach that exploits the topological properties of co-occurrence networks to identify function words. By leveraging the connectivity patterns of function words in these networks, the proposed approach aims to achieve higher precision compared to traditional frequency-based methods. To assess the effectiveness of the network-based approach, we constructed co-occurrence networks for Tetun and Emakhuwa (low-resourced languages), as well as English and Portuguese. We then compared the performance of this approach with traditional frequency-based methods. The results indicate that the network-based approach consistently outperforms traditional methods, with in-degree emerging as the most reliable indicator of function words. This finding suggests promising prospects for automatically generating stopword lists in other low-resource languages, paving the way for developing natural language processing tools for these linguistic contexts.\

Keywords: Stopwords detection, Low-resource languages, Tetun, Emakhuwa.