Abstract

Information extraction (IE) is an important problem in Natural Language Processing (NLP) and Web Mining communities. Recently, IE has been applied to online sex advertisements with the goal of powering search and analytics systems that can help law enforcement investigate human trafficking (HT). Extracting key attributes such as names, phone numbers and addresses from online sex ads is extremely challenging, since such webpages contain boilerplate, obfuscation, and extraneous text in unusual language models. Assessing the quality of an IE system is an important problem that is particularly problematic in this domain due to lack of gold standard datasets. Furthermore, building a robust ground truth from scratch is an expensive and time-consuming task for social scientists and law enforcement to undertake. In this article, we undertake the empirical challenge of analyzing the quality of IE outputs …

Date

2019

Authors

Mayank Kejriwal, Rahul Kapoor

Journal

Applied Network Science

Volume

4

Issue

1

Pages

1-26

Publisher

SpringerOpen