Publications

@article{al-nabki_improving_2020,

title = {Improving named entity recognition in tor darknet with local distance neighbor feature},

author = {Wesam Al-Nabki and Francisco Jáñez-Martino and Andrés Carofilis-Vasco and Eduardo Fidalgo and Javier Velasco-Mata},

url = {https://arxiv.org/abs/2005.08746},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {arXiv preprint arXiv:2005.08746},

abstract = {This paper introduces a novel feature called Local Distance Neighbor (LDN) for named entity recognition in noisy user-generated texts, replacing the need for task-specific and costly gazetteers. The approach was tested on the W-NUT-2017 dataset, achieving state-of-the-art results for Group, Person, and Product categories. By adding 851 manually labeled samples, the method also demonstrated effectiveness in detecting named entities in the Tor Darknet, with F1 scores of 52.96% and 50.57%, aiding Law Enforcement Agencies in identifying entities related to weapons and drug selling.},

keywords = {Gazetteer, NER, Tor Darknet},

pubstate = {published},

tppubtype = {article}

}