Search Results for author:
Found 7 papers, 2 papers with code
“Beste Grüße, Maria Meyer” — Pseudonymization of Privacy-Sensitive Information in Emails
The exploding amount of user-generated content has spurred NLP research to deal with documents from various digital communication formats (tweets, chats, emails, etc.).
Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach
We address the task of distinguishing implicitly abusive sentences on identity groups (“Muslims contaminate our planet”) from other group-related negative polar sentences (“Muslims despise terrorism”).
Implicitly Abusive Language -- What does it actually look like and why are we not getting there?
Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently.
Acquiring a Formality-Informed Lexical Resource for Style Analysis
To track different levels of formality in written discourse, we introduce a novel type of lexicon for the German language, with entries ordered by their degree of (in)formality.
CodE Alltag 2.0 --- A Pseudonymized German-Language Email Corpus
The vast amount of social communication distributed over various electronic media channels (tweets, blogs, emails, etc.
De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus
We deal with the pseudonymization of those stretches of text in emails that might allow to identify real individual persons.
At the Lower End of Language---Exploring the Vulgar and Obscene Side of German
In this paper, we describe a workflow for the data-driven acquisition and semantic scaling of a lexicon that covers lexical items from the lower end of the German language register{---}terms typically considered as rough, vulgar or obscene.
