Michael J. Cafarella

Also published as: Michael Cafarella


Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements
Saeideh Shahrokh Esfahani | Michael J. Cafarella | Maziyar Baran Pouyan | Gregory DeAngelo | Elena Eneva | Andy E. Fano
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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.


TextRunner: Open Information Extraction on the Web
Alexander Yates | Michele Banko | Matthew Broadhead | Michael Cafarella | Oren Etzioni | Stephen Soderland
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)


KnowItNow: Fast, Scalable Information Extraction from the Web
Michael J. Cafarella | Doug Downey | Stephen Soderland | Oren Etzioni
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing