人权研究中的NLP--关于警察和军队及其指挥官的知识图谱提取
NLP in Human Rights Research -- Extracting Knowledge Graphs About Police and Army Units and Their Commanders
摘要：
在这份工作文件中，我们探讨了使用自然语言处理系统，以协助安全部队监察员（SFM）的工作。可持续森林管理创建关于警察、军队和其他安全部队的组织结构、指挥人员和行动的数据，协助人权研究人员、记者和诉讼律师开展工作，帮助查明被指控犯有侵犯人权和违反国际刑法行为的具体单位和人员，并追究其责任。本工作文件提出了一个NLP系统，从英语新闻报道中提取安全部队单位的名称及其人员的传记细节，并推断它们之间的正式关系。与这篇工作论文一起发布的是系统的代码和训练数据集。我们发现，实验NLP系统执行的任务在一个公平的良好的水平。其性能足以证明进一步发展成为一个实时工作流程的合理性，这将使人们深入了解其性能是否转化为时间和资源的节省，从而使其成为一种有效的技术干预。
英文摘要：
In this working paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This working paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this working paper are the system's code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
