Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manually anotated datasets which still do not exist for some languages. In this work we aim to remedy this situation in Slovak by introducing WikiGoldSK, the first sizable human labelled Slovak NER dataset. We benchmark it by evaluating state-of-the-art multilingual Pretrained Language Models and comparing it to the existing silver-standard Slovak NER dataset. We also conduct few-shot experiments and show that training on a sliver-standard dataset yields better results. To enable future work that can be based on Slovak NER, we release the dataset, code, as well as the trained models publicly under permissible licensing terms at https://github.com/NaiveNeuron/WikiGoldSK.

翻译：命名实体识别（NER）是自然语言处理中的基础任务，具有广泛的实际应用。先进NER方法的性能依赖于高质量的人工标注数据集，而部分语言至今仍缺乏此类资源。本研究旨在改善斯洛伐克语的这一现状，通过引入WikiGoldSK——首个大规模人工标注的斯洛伐克语NER数据集。我们利用先进的跨语言预训练语言模型对其进行基准测试，并与现有的银标准斯洛伐克语NER数据集进行对比。此外，我们开展了少样本实验，结果表明基于银标准数据集进行训练可取得更优效果。为促进基于斯洛伐克语NER的未来研究，我们已在许可协议下于https://github.com/NaiveNeuron/WikiGoldSK 公开共享该数据集、代码及训练好的模型。