2025 NAACL NAACL 2025

Make Good Use of GujiRoBERTa to Identify Entities in Ancient Chinese

Abstract

AbstractThis report describes our model submitted for the EvaHan 2025 shared task on named entity recognition for ancient Chinese literary works. Since we participated in the task of closed modality, our method is based on the appointed pretrained language model GujiRoBERTajian-fan and we used appointed datasets.We carried out experiments on decodingstrategies and schedulers to verify the effect of our method. In the final test, our method outperformed the official baseline, demonstrating its effectiveness. In the end, for the results, this report gives an analysis from the perspective of data composition.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio