斯瓦希里语问题和答案数据集对阿弗拉托克辛知识域知识域
Pamela Chogo1, Elizabeth Mkoba1, Neema Kassim1
1Nelson Mandela African Institution of Science and Technology, P.O.Box 447, Tengeru, Arusha, Tanzania.
Data in brief
|April 15, 2025
概括
为了开发自然语言处理 (NLP) 聊天机器人,创建了一个新的斯瓦希里语数据集 - - 关于亚拉托克辛知识问题和答案. 本资源旨在提高农民,贸易商和消费者对非洲毒素污染的认识和知情决策.
科学领域:
- 食品科学与技术 食品科学与技术
- 农业经济学 农业经济学
- 计算语言学 计算语言学
背景情况:
- 非洲毒素污染对粮食安全,人类健康和贸易构成重大风险,特别影响玉米和花生等主食作物.
- 人们对非洲毒素污染的认识和知识有限,这有助于其持续发生,尽管现有意识倡议.
- 自然语言处理 (NLP) 聊天机器人为各种领域的有效知识传播提供了一个有希望的途径.
研究的目的:
- 以斯瓦希里文本为基础的数据集的阿弗拉托克辛知识问题和答案.
- 促进开发基于NLP的聊天机器人,以斯瓦希里语共享非洲毒素信息.
- 增强对阿弗拉托克辛风险的理解和知情决策.
主要方法:
- 数据收集涉及7个焦点小组讨论 (FGD) 与坦桑尼亚六个地区的农民,贸易商和消费者.
- 使用R的定性数据分析确定了6个关键主题,这些主题与非洲毒素知识有关.
- 专家采访提供了对确定的问题的答案,随后以手工验证翻译成斯瓦希里语.
主要成果:
- 一个数据集包括221个配对的问题和答案,分为斯瓦希里语的6个知识领域,被成功开发出来.
- 数据集的结构是为了支持创建一个NLP聊天机器人,以共享亚拉托克辛知识.
- 这项工作有助于为NLP应用程序提供斯瓦希里语数据集.
结论:
- 开发的斯瓦希里语亚拉托克辛知识数据集可以为NLP聊天机器人提供动力,使农民,贸易商,消费者,研究人员和决策者受益.
- 聊天机器人将使人们能够更好地理解和做出明智的决策,以减轻非洲毒素风险.
- 数据集的适应性允许其修改以在其他语言创建类似的NLP工具.
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