21 citations · 22 across the 5 of their papers we have counts for
5 papers
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
Jonas Golde, Felix Hamborg, Alan Akbik
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descr…
Do You Think It's Biased? How To Ask For The Perception Of Media Bias
Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier +3
Media coverage possesses a substantial effect on the public perception of events. The way media frames events can significantly alter the beliefs and perceptions of our society. Ne…
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort
Franziska Weeber, Felix Hamborg, Karsten Donnay +1
Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a to…
Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings
Timo Spinde, Lada Rudnitckaia, Felix Hamborg +1
Slanted news coverage, also called media bias, can heavily influence how news consumers interpret and react to the news. To automatically identify biased language, we present an ex…
ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts
Anastasia Zhukova, Felix Hamborg, Bela Gipp
Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its com…