CrimeProfiler:从新闻媒体中提取和可视化犯罪信息

Tirthankar Dasgupta, Abir Naskar, Rupsa Saha, Lipika Dey
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引用次数: 16

摘要

来自不同来源的新闻文章定期报道犯罪事件,其中包括犯罪细节、被指控实体的信息、调查过程的细节以及最后的判决细节。在本文中,我们提出了自然语言处理技术,用于从数字发布的新闻文章中提取和管理与犯罪相关的信息。我们利用基于计算语言学的方法来分析犯罪相关的新闻文档,以提取不同的犯罪相关实体和事件。这包括罪犯的姓名、受害者的姓名、犯罪性质、地理位置、日期和时间以及对罪犯采取的行动。我们还提出了一种半监督学习技术,从新闻文档中学习不同类别的犯罪事件。这有助于犯罪词典的不断发展。因此,所提出的方法不仅限于侦查已知的犯罪,而且积极地有助于维护更新的犯罪词典。我们用3000篇犯罪报道的新闻文章做了实验。我们实验的最终成果是一个犯罪登记簿,其中包含了跨越地域和时间的犯罪细节。该登记册可进一步用于分析和报告目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CrimeProfiler: crime information extraction and visualization from news media
News articles from different sources regularly report crime incidents that contain details of crime, information about accused entities, details of the investigation process and finally details of judgement. In this paper, we have proposed natural language processing techniques for extraction and curation of crime-related information from digitally published News articles. We have leveraged computational linguistics based methods to analyse crime related News documents to extract different crime related entities and events. This includes name of the criminal, name of the victim, nature of crime, geographic location, date and time, and action taken against the criminal. We have also proposed a semi-supervised learning technique to learn different categories of crime events from the News documents. This helps in continuous evolution of the crime dictionaries. Thus the proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime dictionary. We have done experiments with a collection of 3000 crime-reporting News articles. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.
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