2 citations · 4 across the 5 of their papers we have counts for
5 papers
On Efficient and Statistical Quality Estimation for Data Annotation
Jan-Christoph Klie, Juan Haladjian, Marc Kirchner +1
Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are o…
Analyzing Dataset Annotation Quality Management in the Wild
Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych
Data quality is crucial for training accurate, unbiased, and trustworthy machine learning models as well as for their correct evaluation. Recent works, however, have shown that eve…
Lessons Learned from a Citizen Science Project for Natural Language Processing
Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe +6
Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is…
Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future
Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych
Annotated data is an essential ingredient in natural language processing for training and evaluating machine learning models. It is therefore very desirable for the annotations to…
Annotation Curricula to Implicitly Train Non-Expert Annotators
Ji-Ung Lee, Jan-Christoph Klie, Iryna Gurevych
Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally…