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.
@article{arxiv.2005.08746,
title = {Improving Named Entity Recognition in Tor Darknet with Local Distance Neighbor Feature},
author = {Mhd Wesam Al-Nabki and Francisco Jañez-Martino and Roberto A. Vasco-Carofilis and Eduardo Fidalgo and Javier Velasco-Mata},
journal= {arXiv preprint arXiv:2005.08746},
year = {2020}
}
Comments
2 pages, 1 figure, to be published in conference JNIC 2020