23 citations · 36 across the 14 of their papers we have counts for
4 papers · 1 filter
Know What Not To Know: Users' Perception of Abstaining Classifiers
Andrea Papenmeier, Daniel Hienert, Yvonne Kammerer +2
Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide e…
How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +2
Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, w…
UNDR: User-Needs-Driven Ranking of Products in E-Commerce
Andrea Papenmeier, Daniel Hienert, Firas Sabbah +2
Online retailers often offer a vast choice of products to their customers to filter and browse through. The order in which the products are listed depends on the ranking algorithm…
Evaluation of Word Embeddings for the Social Sciences
Ricardo Schiffers, Dagmar Kern, Daniel Hienert
Word embeddings are an essential instrument in many NLP tasks. Most available resources are trained on general language from Web corpora or Wikipedia dumps. However, word embedding…