Paper Info
Title
Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation.
Abstract
One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own fine-grained in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.
Year
DOI
Venue
2017
10.18653/v1/d17-1275
empirical methods in natural language processing
DocType
Volume
ISSN
Journal
abs/1708.09609
EMNLP (2017) 2598-2607
Citations 
PageRank 
References 
2
0.35
15
Authors
8
Name
Order
Citations
PageRank
Greg Durrett134126.94
Jonathan K. Kummerfeld29316.19
Taylor Berg-Kirkpatrick355435.93
Rebecca S. Portnoff4503.20
sadia afroz527418.85
damon mccoy62073125.49
Kirill Levchenko7123583.12
Vern Paxson8140312130.20