Climate change and political unrest in certain regions of the world are
imposing extreme hardship on many communities and are forcing millions of
vulnerable populations to abandon their homelands and seek refuge in safer
lands. As international laws are not fully set to deal with the migration
crisis, people are relying on networks of exploiting smugglers to escape the
devastation in order to live in stability. During the smuggling journey,
migrants can become victims of human trafficking if they fail to pay the
smuggler and may be forced into coerced labor. Government agencies and
anti-trafficking organizations try to identify the trafficking routes based on
stories of survivors in order to gain knowledge and help prevent such crimes.
In this paper, we propose a system called Narrative to Trajectory (N2T+), which
extracts trajectories of trafficking routes. N2T+ uses Data Science and Natural
Language Processing techniques to analyze trafficking narratives, automatically
extract relevant location names, disambiguate possible name ambiguities, and
plot the trafficking route on a map. In a comparative evaluation we show that
the proposed multi-dimensional approach offers significantly higher geolocation
detection than other state of the art techniques.

气候变化和政治动荡导致许多社区承受极大困难，迫使数百万易受攻击的人口放弃家园，寻求更安全的地区。本文提出了一种名为 N2T+（Narrative to Trajectory）的系统，利用数据科学和自然语言处理技术分析贩运故事，自动提取相关位置名称，并绘制贩运路线图，以帮助防止相关犯罪行为。通过比较评估，我们展示了该多维方法在地理定位识别方面明显优于其他最先进技术。