@inproceedings{shahrokh-esfahani-etal-2019-context,
title = "Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements",
author = "Shahrokh Esfahani, Saeideh and
Cafarella, Michael J. and
Baran Pouyan, Maziyar and
DeAngelo, Gregory and
Eneva, Elena and
Fano, Andy E.",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1114",
doi = "10.18653/v1/P19-1114",
pages = "1180--1184",
abstract = "Human trafficking is a worldwide crisis. Traffickers exploit their victims by anonymously offering sexual services through online advertisements. These ads often contain clues that law enforcement can use to separate out potential trafficking cases from volunteer sex advertisements. The problem is that the sheer volume of ads is too overwhelming for manual processing. Ideally, a centralized semi-automated tool can be used to assist law enforcement agencies with this task. Here, we present an approach using natural language processing to identify trafficking ads on these websites. We propose a classifier by integrating multiple text feature sets, including the publicly available pre-trained textual language model Bi-directional Encoder Representation from transformers (BERT). In this paper, we demonstrate that a classifier using this composite feature set has significantly better performance compared to any single feature set alone.",
}
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<abstract>Human trafficking is a worldwide crisis. Traffickers exploit their victims by anonymously offering sexual services through online advertisements. These ads often contain clues that law enforcement can use to separate out potential trafficking cases from volunteer sex advertisements. The problem is that the sheer volume of ads is too overwhelming for manual processing. Ideally, a centralized semi-automated tool can be used to assist law enforcement agencies with this task. Here, we present an approach using natural language processing to identify trafficking ads on these websites. We propose a classifier by integrating multiple text feature sets, including the publicly available pre-trained textual language model Bi-directional Encoder Representation from transformers (BERT). In this paper, we demonstrate that a classifier using this composite feature set has significantly better performance compared to any single feature set alone.</abstract>
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%0 Conference Proceedings
%T Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements
%A Shahrokh Esfahani, Saeideh
%A Cafarella, Michael J.
%A Baran Pouyan, Maziyar
%A DeAngelo, Gregory
%A Eneva, Elena
%A Fano, Andy E.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F shahrokh-esfahani-etal-2019-context
%X Human trafficking is a worldwide crisis. Traffickers exploit their victims by anonymously offering sexual services through online advertisements. These ads often contain clues that law enforcement can use to separate out potential trafficking cases from volunteer sex advertisements. The problem is that the sheer volume of ads is too overwhelming for manual processing. Ideally, a centralized semi-automated tool can be used to assist law enforcement agencies with this task. Here, we present an approach using natural language processing to identify trafficking ads on these websites. We propose a classifier by integrating multiple text feature sets, including the publicly available pre-trained textual language model Bi-directional Encoder Representation from transformers (BERT). In this paper, we demonstrate that a classifier using this composite feature set has significantly better performance compared to any single feature set alone.
%R 10.18653/v1/P19-1114
%U https://aclanthology.org/P19-1114
%U https://doi.org/10.18653/v1/P19-1114
%P 1180-1184
Markdown (Informal)
[Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements](https://aclanthology.org/P19-1114) (Shahrokh Esfahani et al., ACL 2019)
ACL