  Classic information extraction techniques consist in building questions and
answers about the facts. Indeed, it is still a challenge to subjective
information extraction systems to identify opinions and feelings in context. In
sentiment-based NLP tasks, there are few resources to information extraction,
above all offensive or hateful opinions in context. To fill this important gap,
this short paper provides a new cross-lingual and contextual offensive lexicon,
which consists of explicit and implicit offensive and swearing expressions of
opinion, which were annotated in two different classes: context dependent and
context-independent offensive. In addition, we provide markers to identify hate
speech. Annotation approach was evaluated at the expression-level and achieves
high human inter-annotator agreement. The provided offensive lexicon is
available in Portuguese and English languages.
