The proliferation of online offensive language necessitates the development
of effective detection mechanisms, especially in multilingual contexts. This
study addresses the challenge by developing and introducing novel datasets for
offensive language detection in three major Nigerian languages: Hausa, Yoruba,
and Igbo. We collected data from Twitter and manually annotated it to create
datasets for each of the three languages, using native speakers. We used
pre-trained language models to evaluate their efficacy in detecting offensive
language in our datasets. The best-performing model achieved an accuracy of
90\%. To further support research in offensive language detection, we plan to
make the dataset and our models publicly available.

为了在多语言环境中有效检测网络攻击性言论，本研究使用来自 Twitter 的数据，并通过手动注释创建了三种主要尼日利亚语言（豪萨语、约鲁巴语和伊博语）的侮辱性言论检测的新数据集。我们使用预训练的语言模型评估其在我们的数据集中检测侮辱性言论的有效性，最佳模型的准确率达到 90％。为了进一步支持侮辱性言论检测的研究，我们计划公开数据集和模型。

一个用于豪萨、约鲁巴和伊博语言的冒犯性语言和仇恨言论检测的多语言数据集

A multilingual dataset for offensive language and hate speech detection  for hausa, yoruba and igbo languages

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 this https URL and an interactive web app
this https URL

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

NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian  Languages

Many Nigerian languages have relinquished their previous prestige and purpose
in modern society to English and Nigerian Pidgin. For the millions of L1
speakers of indigenous languages, there are inequalities that manifest
themselves as unequal access to information, communications, health care,
security as well as attenuated participation in political and civic life. To
minimize exclusion and promote socio-linguistic and economic empowerment, this
work explores the feasibility of Neural Machine Translation (NMT) for the Edoid
language family of Southern Nigeria. Using the new JW300 public dataset, we
trained and evaluated baseline translation models for four widely spoken
languages in this group: \`Ed\'o, \'Es\'an, Urhobo and Isoko. Trained models,
code and datasets have been open-sourced to advance future research efforts on
Edoid language technology.

本文探讨在现代社会中，许多尼日利亚语言已经失去之前的声望和用途，转而使用英语和尼日利亚皮钦语，导致数百万母语为土著语言的人无法公平地获取信息、沟通、医疗、安全和参与政治民生事务等方面存在的不平等。为了减少排斥并促进社会语言和经济赋权，本文探讨使用神经机器翻译技术在尼日利亚南部的 Edoid 语系中的可行性。使用新的 JW300 公共数据集，本文训练和评估了此组中四种常用语言（Ed'o，'Es'an，Urhobo 和 Isoko）的基线翻译模型，并将训练模型、代码和数据集开源以推进未来的 Edoid 语言技术研究。