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In this paper, we describe and study the indicator mining problem in theonline sex advertising domain. We present an in-development system, FlagIt(Flexible and adaptive generation of Indicators from text), which combines thebenefits of both a lightweight expert system and classical semi-supervision(heuristic re-labeling) with recently released state-of-the-art unsupervisedtext embeddings to tag millions of sentences with indicators that are highlycorrelated with human trafficking. The FlagIt technology stack is open source.On preliminary evaluations involving five indicators, FlagIt illustratespromising performance compared to several alternatives. The system is beingactively developed, refined and integrated into a domain-specific search systemused by over 200 law enforcement agencies to combat human trafficking, and isbeing aggressively extended to mine at least six more indicators with minimalprogramming effort. FlagIt is a good example of a system that operates inlimited label settings, and that requires creative combinations of establishedmachine learning techniques to produce outputs that could be used by real-worldnon-technical analysts.

FlagIt: A System for Minimally Supervised Human Trafficking Indicator Mining