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Found 31 papers, 8 papers with code
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments
We present a new dataset comprising tweets for the novel task of detecting biographically relevant utterances.
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”).
Overview of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments.
“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.).
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.
Implicitly Abusive Comparisons -- A New Dataset and Linguistic Analysis
We examine the task of detecting implicitly abusive comparisons (e. g. {``}Your hair looks like you have been electrocuted{''}).
Exploiting Emojis for Abusive Language Detection
We propose to use abusive emojis, such as the {``}middle finger{''} or {``}face vomiting{''}, as a proxy for learning a lexicon of abusive words.
Effective Slot Filling Based on Shallow Distant Supervision Methods
In the TAC KBP 2013 English Slotfilling evaluation, the submitted main run of the LSV RelationFactory system achieved the top-ranked F1-score of 37. 3%.
A Gold Standard for Relation Extraction in the Food Domain
We present a gold standard for semantic relation extraction in the food domain for German.
MLSA --- A Multi-layered Reference Corpus for German Sentiment Analysis
The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity.
