1 code implementation • EMNLP (ArgMining) 2021 • Julia Romberg, Stefan Conrad
In our evaluation, we achieve high macro F1 scores (0. 76 - 0. 80 for the identification of argumentative units; 0. 86 - 0. 93 for their classification) on all datasets.
no code implementations • SMM4H (COLING) 2020 • Julia Romberg, Jan Dyczmons, Sandra Olivia Borgmann, Jana Sommer, Markus Vomhof, Cecilia Brunoni, Ismael Bruck-Ramisch, Luis Enders, Andrea Icks, Stefan Conrad
First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0. 89 (Krippendorff’s α).
no code implementations • SIGDIAL (ACL) 2022 • Lea Kawaletz, Heidrun Dorgeloh, Stefan Conrad, Zeljko Bekcic
Corpora of argumentative discourse are commonly analyzed in terms of argumentative units, consisting of claims and premises.
no code implementations • 29 May 2024 • Manh Khoi Duong, Stefan Conrad
Motivated by the recital (67) of the current corrigendum of the AI Act in the European Union, we propose and present measures and mitigation strategies for discrimination in tabular datasets.
no code implementations • 21 May 2024 • Manh Khoi Duong, Stefan Conrad
By introducing a multi-objective optimization problem that considers fairness and data loss, we propose a methodology to find Pareto-optimal solutions that balance these objectives.
1 code implementation • 1 Apr 2022 • Carlo Schackow, Stefan Conrad, Ingo Plag
This paper presents a novel approach to this specific problem of word sense disambiguation: set expansion.
1 code implementation • 26 Jun 2020 • Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling
Alpha matting aims to estimate the translucency of an object in a given image.
1 code implementation • 25 Mar 2020 • Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling
Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough sketch.
no code implementations • SEMEVAL 2019 • Alex Oberstrass, er, Julia Romberg, Anke Stoll, Stefan Conrad
We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6).
no code implementations • SEMEVAL 2018 • Matthias Liebeck, Andreas Funke, Stefan Conrad
This paper describes our participation in the SemEval-2018 Task 12 Argument Reasoning Comprehension Task which calls to develop systems that, given a reason and a claim, predict the correct warrant from two opposing options.
no code implementations • SEMEVAL 2017 • Tobias Cabanski, Julia Romberg, Stefan Conrad
In this Paper a system for solving SemEval-2017 Task 5 is presented.
no code implementations • 3 Jan 2017 • Pashutan Modaresi, Philipp Gross, Siavash Sefidrodi, Mirja Eckhof, Stefan Conrad
In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario.