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.
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.
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.