自然语言处理方法在白领犯罪调查中的性能评估
Performance Evaluation of a Natural Language Processing approach applied in White Collar crime investigation
摘要：
在当今世界，我们每天都面临着来自各种来源的越来越多的信息。人们和合作正在大规模地产生数据，自从互联网、电子邮件和社交媒体兴起以来，产生的数据量呈指数级增长。从执法的角度来看，当对个人或公司进行刑事调查时，我们必须处理这些大量的数据。需要回答有关的问题，如谁犯了罪，谁参与了，发生了什么，什么时候，谁在沟通，关于什么？不仅可供调查的数据量大大增加，而且这些数据的复杂性也增加了。当这些通信模式需要与例如被扣押的财务管理或公司文件相结合时，就会产生复杂的调查问题。最近，刑事调查人员面临着巨大的挑战，需要在大数据环境中找到犯罪证据，他们必须处理大型复杂的数据集，特别是在金融和欺诈调查中。为了解决这个问题，欧洲某国的金融和欺诈调查部门开发了一种名为LES的新工具，该工具使用自然语言处理（NLP）技术来帮助刑事调查人员以更有效和更快的方式处理大量文本信息。在本文中，我们简要介绍了这个工具，我们专注于评估其性能方面的要求，法医调查：速度，更聪明，更容易为调查人员。为了评估这个LES工具，我们使用不同的性能指标。我们还显示了我们的评估与大型和复杂的数据集，从现实世界的应用程序的实验结果。
英文摘要：
In today world we are confronted with increasing amounts of information every day coming from a large variety of sources. People and co-operations are producing data on a large scale, and since the rise of the internet, e-mail and social media the amount of produced data has grown exponentially. From a law enforcement perspective we have to deal with these huge amounts of data when a criminal investigation is launched against an individual or company. Relevant questions need to be answered like who committed the crime, who were involved, what happened and on what time, who were communicating and about what? Not only the amount of available data to investigate has increased enormously, but also the complexity of this data has increased. When these communication patterns need to be combined with for instance a seized financial administration or corporate document shares a complex investigation problem arises. Recently, criminal investigators face a huge challenge when evidence of a crime needs to be found in the Big Data environment where they have to deal with large and complex datasets especially in financial and fraud investigations. To tackle this problem, a financial and fraud investigation unit of a European country has developed a new tool named LES that uses Natural Language Processing (NLP) techniques to help criminal investigators handle large amounts of textual information in a more efficient and faster way. In this paper, we present briefly this tool and we focus on the evaluation its performance in terms of the requirements of forensic investigation: speed, smarter and easier for investigators. In order to evaluate this LES tool, we use different performance metrics. We also show experimental results of our evaluation with large and complex datasets from real-world application.
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。
