利用局部距离邻域特征改进Tor暗网中的命名实体识别
Improving Named Entity Recognition in Tor Darknet with Local Distance Neighbor Feature
摘要：
在嘈杂的用户生成的文本中识别名称实体是一项困难的任务，通常通过纳入地名录等外部信息资源来加强。然而，地名录是针对特定任务的，建造和维护费用昂贵。本文采用并改进了Aguilar等人的方法，提出了一种新的功能，称为本地距离邻居，它取代地名录。我们在W-NUT-2017数据集上测试了新方法，获得了命名实体的集团，人员和产品类别的最新结果。接下来，我们将851个手动标记的样本添加到W-NUT-2017数据集中，以说明Tor Darknet中与武器和毒品销售相关的命名实体。最后，我们的提案在这个扩展数据集上实现了实体和表面F1分数分别为52.96%和50.57%，证明了它对执法机构检测Tor隐藏服务中的命名实体的有用性。
英文摘要：
Name entity recognition in noisy user-generated texts is a difficult task usually enhanced by incorporating an external resource of information, such as gazetteers. However, gazetteers are task-specific, and they are expensive to build and maintain. This paper adopts and improves the approach of Aguilar et al. by presenting a novel feature, called Local Distance Neighbor, which substitutes gazetteers. We tested the new approach on the W-NUT-2017 dataset, obtaining state-of-the-art results for the Group, Person and Product categories of Named Entities. Next, we added 851 manually labeled samples to the W-NUT-2017 dataset to account for named entities in the Tor Darknet related to weapons and drug selling. Finally, our proposal achieved an entity and surface F1 scores of 52.96% and 50.57% on this extended dataset, demonstrating its usefulness for Law Enforcement Agencies to detect named entities in the Tor hidden services.
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。
