HiligayNER: A Baseline Named Entity Recognition Model for Hiligaynon
This work addresses the underrepresentation of Hiligaynon in language processing, providing a baseline for future research in regional Philippine languages, though it is incremental as it applies existing methods to new data.
The study tackled the lack of annotated corpora and baseline models for Hiligaynon by introducing HiligayNER, the first publicly available NER model for this language, achieving over 80% in precision, recall, and F1-score across entity types.
The language of Hiligaynon, spoken predominantly by the people of Panay Island, Negros Occidental, and Soccsksargen in the Philippines, remains underrepresented in language processing research due to the absence of annotated corpora and baseline models. This study introduces HiligayNER, the first publicly available baseline model for the task of Named Entity Recognition (NER) in Hiligaynon. The dataset used to build HiligayNER contains over 8,000 annotated sentences collected from publicly available news articles, social media posts, and literary texts. Two Transformer-based models, mBERT and XLM-RoBERTa, were fine-tuned on this collected corpus to build versions of HiligayNER. Evaluation results show strong performance, with both models achieving over 80% in precision, recall, and F1-score across entity types. Furthermore, cross-lingual evaluation with Cebuano and Tagalog demonstrates promising transferability, suggesting the broader applicability of HiligayNER for multilingual NLP in low-resource settings. This work aims to contribute to language technology development for underrepresented Philippine languages, specifically for Hiligaynon, and support future research in regional language processing.