15 citations · 74 across the 11 of their papers we have counts for
13 papers
Towards a Holistic View on Argument Quality Prediction
Michael Fromm, Max Berrendorf, Johanna Reiml +4
Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing at…
Active Learning for Argument Strength Estimation
Nataliia Kees, Michael Fromm, Evgeniy Faerman +1
High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument…
Prediction of soft proton intensities in the near-Earth space using machine learning
Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher +14
The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have deriv…
Argument Mining Driven Analysis of Peer-Reviews
Michael Fromm, Evgeniy Faerman, Max Berrendorf +7
Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process a…
Diversity Aware Relevance Learning for Argument Search
Michael Fromm, Max Berrendorf, Sandra Obermeier +2
In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims…
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov +1
Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently re…