15 citations · 74 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
A Critical Assessment of State-of-the-Art in Entity Alignment
Max Berrendorf, Ludwig Wacker, Evgeniy Faerman
In this work, we perform an extensive investigation of two state-of-the-art (SotA) methods for the task of Entity Alignment in Knowledge Graphs. Therefore, we first carefully exami…
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
Julian Busch, Evgeniy Faerman, Matthias Schubert +1
Subspace clustering has established itself as a state-of-the-art approach to clustering high-dimensional data. In particular, methods relying on the self-expressiveness property ha…
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
Max Berrendorf, Evgeniy Faerman, Laurent Vermue +1
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the curre…