Title: Information Extraction in Illicit Domains,
Abstract: 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.