论文标题
从野生动植物贩运报告中提取和可视化野生动植物贩运事件
Extracting and Visualizing Wildlife Trafficking Events from Wildlife Trafficking Reports
论文作者
论文摘要
专家通过有关癫痫发作和逮捕的文章手动筛选野生动植物贩运的专家，这很耗时，使识别趋势变得困难。我们应用自然语言处理技术来自动从生态活动家为治理和执法人员（EAGLE）发布的报告中提取数据。我们扩展了Python Spacy的预培训管道，并添加了一个自定义的命名实体统治者，该统治者在15个报告中针对现有的基线确定了15个完全正确的事件，并且没有确定任何完全正确的事件。提取的野生动植物贩运事件被插入数据库。然后，我们创建了可视化，以随着时间的推移和整个区域展示趋势，以支持域专家。这些可以在我们的网站上访问非洲的野生动植物贩运（https://wildlifemqp.github.io/visalizatization/）。
Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult. We apply natural language processing techniques to automatically extract data from reports published by the Eco Activists for Governance and Law Enforcement (EAGLE). We expanded Python spaCy's pre-trained pipeline and added a custom named entity ruler, which identified 15 fully correct and 36 partially correct events in 15 reports against an existing baseline, which did not identify any fully correct events. The extracted wildlife trafficking events were inserted to a database. Then, we created visualizations to display trends over time and across regions to support domain experts. These are accessible on our website, Wildlife Trafficking in Africa (https://wildlifemqp.github.io/Visualizations/).
