# Information Extraction in Illicit Domains

arXiv: https://arxiv.org/abs/1703.03097 · HTML: https://mlsanity.com/paper/1703.03097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Knowledge mining of unstructured information: application to cyber-domain](https://mlsanity.com/paper/2109.03848/) (2021)
- [A Knowledge Engineering Primer](https://mlsanity.com/paper/2305.17196/) (2023)
- [A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain](https://mlsanity.com/paper/2310.05258/) (2023)
- [Scraping the Shadows: Deep Learning Breakthroughs in Dark Web Intelligence](https://mlsanity.com/paper/2504.02872/) (2025)

## Abstract (arXiv)

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.