1 code implementation • Findings (EMNLP) 2021 • Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa
Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.
no code implementations • GWC 2018 • Terry Ruas, William Grosky
For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document.
no code implementations • 17 Apr 2024 • Frederic Kirstein, Jan Philip Wahle, Terry Ruas, Bela Gipp
Meeting summarization has become a critical task considering the increase in online interactions.
no code implementations • 27 Feb 2024 • Tomáš Horych, Martin Wessel, Jan Philip Wahle, Terry Ruas, Jerome Waßmuth, André Greiner-Petter, Akiko Aizawa, Bela Gipp, Timo Spinde
MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models.
1 code implementation • 19 Feb 2024 • Jan Philip Wahle, Terry Ruas, Mohamed Abdalla, Bela Gipp, Saif M. Mohammad
This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023).
1 code implementation • 26 Dec 2023 • Timo Spinde, Smi Hinterreiter, Fabian Haak, Terry Ruas, Helge Giese, Norman Meuschke, Bela Gipp
However, we have identified a lack of interdisciplinarity in existing projects, and a need for more awareness of the various types of media bias to support methodologically thorough performance evaluations of media bias detection systems.
1 code implementation • 23 Oct 2023 • Jan Philip Wahle, Terry Ruas, Mohamed Abdalla, Bela Gipp, Saif M. Mohammad
We analyzed ~77k NLP papers, ~3. 1m citations from NLP papers to other papers, and ~1. 8m citations from other papers to NLP papers.
1 code implementation • 23 Oct 2023 • Jan Philip Wahle, Bela Gipp, Terry Ruas
Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language.
1 code implementation • 4 May 2023 • Mohamed Abdalla, Jan Philip Wahle, Terry Ruas, Aurélie Névéol, Fanny Ducel, Saif M. Mohammad, Karën Fort
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development.
1 code implementation • 25 Apr 2023 • Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde
A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.
1 code implementation • 24 Mar 2023 • Jonas Becker, Jan Philip Wahle, Terry Ruas, Bela Gipp
Additionally, we identify four datasets as the most diverse and challenging for paraphrase detection.
no code implementations • 7 Nov 2022 • Timo Spinde, Jan-David Krieger, Terry Ruas, Jelena Mitrović, Franz Götz-Hahn, Akiko Aizawa, Bela Gipp
Media has a substantial impact on the public perception of events.
1 code implementation • 26 Oct 2022 • Frederic Kirstein, Jan Philip Wahle, Terry Ruas, Bela Gipp
Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization.
2 code implementations • 13 Oct 2022 • Terry Ruas, Jan Philip Wahle, Lennart Küll, Saif M. Mohammad, Bela Gipp
This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives.
3 code implementations • 7 Oct 2022 • Jan Philip Wahle, Terry Ruas, Frederic Kirstein, Bela Gipp
The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work.
1 code implementation • 29 Sep 2022 • Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa
Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.
1 code implementation • 22 May 2022 • Jan-David Krieger, Timo Spinde, Terry Ruas, Juhi Kulshrestha, Bela Gipp
We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0. 814.
1 code implementation • LREC 2022 • Jan Philip Wahle, Terry Ruas, Saif M. Mohammad, Bela Gipp
We present an initial analysis focused on the volume of computer science research (e. g., number of papers, authors, research activity), trends in topics of interest, and citation patterns.
1 code implementation • 28 Mar 2022 • Malte Ostendorff, Till Blume, Terry Ruas, Bela Gipp, Georg Rehm
We compare and analyze three generic document embeddings, six specialized document embeddings and a pairwise classification baseline in the context of research paper recommendations.
1 code implementation • 18 Nov 2021 • Johannes Stegmüller, Fabian Bauer-Marquart, Norman Meuschke, Terry Ruas, Moritz Schubotz, Bela Gipp
Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations.
1 code implementation • 15 Nov 2021 • Jan Philip Wahle, Nischal Ashok, Terry Ruas, Norman Meuschke, Tirthankar Ghosal, Bela Gipp
We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.
2 code implementations • 15 Jun 2021 • Jan Philip Wahle, Terry Ruas, Norman Meuschke, Bela Gipp
We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs).
1 code implementation • 28 Apr 2021 • Malte Ostendorff, Elliott Ash, Terry Ruas, Bela Gipp, Julian Moreno-Schneider, Georg Rehm
Simultaneously, legal recommender systems are typically evaluated in small-scale user study without any public available benchmark datasets.
no code implementations • 23 Mar 2021 • Jan Philip Wahle, Terry Ruas, Norman Meuschke, Bela Gipp
The rise of language models such as BERT allows for high-quality text paraphrasing.
2 code implementations • 22 Mar 2021 • Jan Philip Wahle, Terry Ruas, Tomáš Foltýnek, Norman Meuschke, Bela Gipp
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity.
1 code implementation • 22 Jan 2021 • Terry Ruas, Charles Henrique Porto Ferreira, William Grosky, Fabrício Olivetti de França, Débora Maria Rossi Medeiros
The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually.
1 code implementation • 21 Jan 2021 • Terry Ruas, William Grosky, Akiko Aizawa
Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data.
1 code implementation • COLING 2020 • Malte Ostendorff, Terry Ruas, Till Blume, Bela Gipp, Georg Rehm
Our findings motivate future research of aspect-based document similarity and the development of a recommender system based on the evaluated techniques.
4 code implementations • 22 Mar 2020 • Malte Ostendorff, Terry Ruas, Moritz Schubotz, Georg Rehm, Bela Gipp
In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task.
no code implementations • 20 May 2019 • André Greiner-Petter, Terry Ruas, Moritz Schubotz, Akiko Aizawa, William Grosky, Bela Gipp
Nowadays, Machine Learning (ML) is seen as the universal solution to improve the effectiveness of information retrieval (IR) methods.