We present the development of a dataset for Kazakh named entity recognition. The dataset was built as there is a clear need for publicly available annotated corpora in Kazakh, as well as annotation guidelines containing straightforward--but rigorous--rules and examples. The dataset annotation, based on the IOB2 scheme, was carried out on television news text by two native Kazakh speakers under the supervision of the first author. The resulting dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. State-of-the-art machine learning models to automatise Kazakh named entity recognition were also built, with the best-performing model achieving an exact match F1-score of 97.22% on the test set. The annotated dataset, guidelines, and codes used to train the models are freely available for download under the CC BY 4.0 licence from https://github.com/IS2AI/KazNERD.

翻译：建立该数据集是因为显然需要公开提供哈萨克语的附加说明公司,以及含有直截了当但严格的规则和实例的说明准则; 以IOB2计划为基础的数据集注释,由两位哈萨克本土发言人在第一作者的监督下通过电视新闻文本进行; 由此产生的数据集包含112,702个判决和25个实体类别的136,333个说明; 还建立了将哈萨克语命名实体的识别自动化的先进机器学习模型,最先进的模型在测试集上实现了97.22%的精确匹配F1核心; 用于培训模型的附加说明数据集、准则和代码,可在https://github.com/IS2AI/KazNERD的CC 4.0许可下免费下载。