WikiGoldSK：面向斯洛伐克语命名实体识别的标注数据集、基线与少样本学习实验
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity Recognition
AI总结：
本文针对斯洛伐克语缺乏高质量人工标注NER数据集的问题，构建了首个大规模人工标注数据集WikiGoldSK，通过多语言预训练模型评估基准与少样本实验，发现银标准数据集训练效果更优，并公开了数据集与模型以促进未来研究。
摘要：
命名实体识别（NER）是一项具有广泛实际应用的基础NLP任务。最先进NER方法的性能依赖于高质量的人工标注数据集，而某些语言仍缺乏此类数据集。在本研究中，我们通过引入WikiGoldSK（首个具备相当规模的人工标注斯洛伐克语NER数据集），旨在弥补斯洛伐克语的这一状况。我们通过评估最先进的多语言预训练语言模型，并将其与现有的银标准斯洛伐克语NER数据集进行比较，对该数据集进行基准测试。我们还进行了少样本实验，结果表明在银标准数据集上训练能产生更好的结果。为支持基于斯洛伐克语NER的未来研究，我们在许可的授权条款下公开发布了数据集、代码及训练好的模型。
英文摘要：
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.
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。
