HiligayNER：面向Hiligaynon语的基线命名实体识别模型
HiligayNER: A Baseline Named Entity Recognition Model for Hiligaynon
AI总结：
针对Hiligaynon语在语言处理中代表不足的问题，本研究推出首个基于8,000余条标注句子微调mBERT和XLM-RoBERTa构建的公开基线命名实体识别模型HiligayNER，其精确率、召回率和F1分数均超80%，并展现出良好的跨语言迁移能力。
摘要：
Hiligaynon语主要在菲律宾的Panay岛、Negros Occidental和Soccsksargen地区使用，由于缺乏标注语料库和基线模型，该语言在语言处理研究中仍处于代表不足的状态。本研究推出了HiligayNER，这是首个用于Hiligaynon语命名实体识别（NER）任务的公开基线模型。用于构建HiligayNER的数据集包含从公开的新闻文章、社交媒体帖子和文学文本中收集的8,000多条标注句子。两个基于Transformer的模型mBERT和XLM-RoBERTa在此收集的语料库上进行了微调，以构建HiligayNER的版本。评估结果显示出强劲的性能，两种模型在各类实体上的精确率、召回率和F1分数均超过80%。此外，使用Cebuano语和Tagalog语进行的跨语言评估展示了良好的可迁移性，表明HiligayNER在低资源环境下的多语言NLP中具有更广泛的适用性。本研究旨在为代表性不足的菲律宾语言（特别是Hiligaynon语）的语言技术开发做出贡献，并支持未来在区域语言处理方面的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
