This paper describes Adam Mickiewicz University's (AMU) solution for the 4th Shared Task on SlavNER. The task involves the identification, categorization, and lemmatization of named entities in Slavic languages. Our approach involved exploring the use of foundation models for these tasks. In particular, we used models based on the popular BERT and T5 model architectures. Additionally, we used external datasets to further improve the quality of our models. Our solution obtained promising results, achieving high metrics scores in both tasks. We describe our approach and the results of our experiments in detail, showing that the method is effective for NER and lemmatization in Slavic languages. Additionally, our models for lemmatization will be available at: https://huggingface.co/amu-cai.

翻译：本文描述了亚当·密茨凯维奇大学（AMU）在第四届斯拉夫语命名实体识别共享任务（SlavNER）中的解决方案。该任务涉及对斯拉夫语中命名实体的识别、分类及词形还原。我们的方法是探索使用基础模型来处理这些任务。具体而言，我们采用了基于主流BERT和T5模型架构的模型。此外，我们利用外部数据集进一步提升了模型质量。我们的解决方案取得了显著成果，在两项任务中均实现了较高的评价指标分数。我们详细描述了所用方法及实验结果，表明该方法对斯拉夫语的命名实体识别与词形还原任务具有有效性。同时，我们的词形还原模型将开放于：https://huggingface.co/amu-cai。