Most of the common applications of Named Entity Recognition (NER) is on English and other highly available languages. In this work, we present our findings on Named Entity Recognition for 5 Nigerian Languages (Nigerian English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are considered low-resourced, and very little openly available Natural Language Processing work has been done in most of them. In this work, individual NER models were trained and metrics recorded for each of the languages. We also worked on a combined model that can handle Named Entity Recognition (NER) for any of the five languages. The combined model works well for Named Entity Recognition(NER) on each of the languages and with better performance compared to individual NER models trained specifically on annotated data for the specific language. The aim of this work is to share our learning on how information extraction using Named Entity Recognition can be optimized for the listed Nigerian Languages for inclusion, ease of deployment in production and reusability of models. Models developed during this project are available on GitHub https://git.io/JY0kk and an interactive web app https://nigner.herokuapp.com/.

该研究研究了5种尼日利亚语言（尼日利亚英语，尼日利亚皮钦英语，伊博语，约鲁巴语和豪萨语）的命名实体识别，在建立个体模型和组合模型的基础上，提高了信息提取的效率，提高了模型的再利用性和生产部署的便利性。

NaijaNER: 面向尼日利亚5种语言的全面命名实体识别