This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).

翻译：本文介绍了一种用于安全预防的IncidentAI数据集。与通常仅包含单一任务的先前语料库不同，我们的数据集包含三个任务：命名实体识别、因果抽取和信息检索。该数据集由至少拥有六年高压气体保护管理实践经验的专业领域专家进行标注。我们在安全预防场景中验证了该数据集的贡献。三个任务的初步结果表明，自然语言处理技术有助于分析事故报告以预防未来故障。该数据集促进了自然语言处理与事故管理领域的未来研究。同时提供了数据集的访问方式（IncidentAI数据集可访问：https://github.com/Cinnamon/incident-ai-dataset）。