no code implementations • SemEval (NAACL) 2022 • Stefan Heil, Karina Kopp, Albin Zehe, Konstantin Kobs, Andreas Hotho
This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity.
no code implementations • SemEval (NAACL) 2022 • Dirk Wangsadirdja, Felix Heinickel, Simon Trapp, Albin Zehe, Konstantin Kobs, Andreas Hotho
We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively.
no code implementations • EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 • David Schmidt, Albin Zehe, Janne Lorenzen, Lisa Sergel, Sebastian Düker, Markus Krug, Frank Puppe
The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers.
no code implementations • 11 May 2023 • Albin Zehe, Julian Schröter, Andreas Hotho
Suspense is an important tool in storytelling to keep readers engaged and wanting to read more.
no code implementations • 14 Oct 2022 • Vincenzo Perri, Lisi Qarkaxhija, Albin Zehe, Andreas Hotho, Ingo Scholtes
Natural Language Processing and Machine Learning have considerably advanced Computational Literary Studies.
no code implementations • EACL 2021 • Albin Zehe, Leonard Konle, Lea Katharina D{\"u}mpelmann, Evelyn Gius, Andreas Hotho, Fotis Jannidis, Lucas Kaufmann, Markus Krug, Frank Puppe, Nils Reiter, Annekea Schreiber, Nathalie Wiedmer
This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Konstantin Kobs, Tobias Koopmann, Albin Zehe, David Fernes, Philipp Krop, Andreas Hotho
Whenever researchers write a paper, the same question occurs: {``}Where to submit?
no code implementations • LREC 2020 • Daniel Schl{\"o}r, Albin Zehe, Konstantin Kobs, Blerta Veseli, Franziska Westermeier, Larissa Br{\"u}bach, Daniel Roth, Marc Erich Latoschik, Andreas Hotho
Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories.
1 code implementation • 6 Mar 2020 • Konstantin Kobs, Michael Steininger, Albin Zehe, Florian Lautenschlager, Andreas Hotho
One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally.
no code implementations • 18 Feb 2020 • Michael Steininger, Konstantin Kobs, Albin Zehe, Florian Lautenschlager, Martin Becker, Andreas Hotho
In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data.
no code implementations • SEMEVAL 2019 • Albin Zehe, Lena Hettinger, Stefan Ernst, Christian Hauptmann, Andreas Hotho
This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection.
no code implementations • SEMEVAL 2018 • Lena Hettinger, Alex Dallmann, er, Albin Zehe, Thomas Niebler, Andreas Hotho
In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1. 1) and noisy data (subtask 1. 2).
no code implementations • 16 Apr 2018 • Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho
Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75. 11% for the first subtask and 81. 44% for the second.
no code implementations • 28 Nov 2016 • Fotis Jannidis, Isabella Reger, Albin Zehe, Martin Becker, Lena Hettinger, Andreas Hotho
With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels.