Underground forums are widely used by criminals to buy and sell a host of stolen items,
datasets, resources, and criminal services. These forums contain important resources for …

We study a family of data augmentation methods, substructure substitution (SUB2), for
natural language processing (NLP) tasks. SUB2 generates new examples by substituting …

Recently, pre-trained language models have achieved remarkable success in a broad range
of natural language processing tasks. However, in multilingual setting, it is extremely …

This article presents a discussion on the main linguistic phenomena which cause difficulties
in the analysis of user-generated texts found on the web and in social media, and proposes …

Darknet markets are online services behind Tor where cybercriminals trade illegal goods
and stolen datasets. In recent years, security analysts and law enforcement start to …

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 …

The paper presents a discussion on the main linguistic phenomena of user-generated texts
found in web and social media, and proposes a set of annotation guidelines for their …

In this work we present a taxonomy of error categories for lexical normalization, which is the
task of translating user generated content to canonical language. We annotate a recent …

Growing IT complexity and related problems, which are reflected in IT tickets, create a need
for new qualitative approaches. The goal is to automate the extraction of main topics …

User-generated content presents many challenges for its automatic processing. While many
of them do come from out-of-vocabulary effects, others spawn from different linguistic …