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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 11 of 11 1 On Classifying whether Two Texts are on the Same Side of an Argument ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hakimi, Ahmad Dawar; Heyer, Gerhard; Körner, Erik; Potthast, Martin; Wiedemann, Gregor. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.795/ Abstract: To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task's hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by ... Keyword: Language Models; Natural Language Processing; Semantic Evaluation; Sociolinguistics URL: https://underline.io/lecture/37386-on-classifying-whether-two-texts-are-on-the-same-side-of-an-argument https://dx.doi.org/10.48448/wmx4-0145 BASE Hide details 2 Multi-modal page stream segmentation with convolutional neural networks [<Journal>] Wiedemann, Gregor [Verfasser]; Heyer, Gerhard [Verfasser] DNB Subject Category Language Show details 3 Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter Biemann, Chris; Ruppert, Eugen; Wiedemann, Gregor... In: http://hw.oeaw.ac.at/8435-5 (2018) BASE Show details 4 A Multilingual Information Extraction Pipeline for Investigative Journalism ... Wiedemann, Gregor; Yimam, Seid Muhie; Biemann, Chris. - : arXiv, 2018 BASE Show details 5 New/s/leak 2.0 - Multilingual Information Extraction and Visualization for Investigative Journalism ... Wiedemann, Gregor; Yimam, Seid Muhie; Biemann, Chris. - : arXiv, 2018 BASE Show details 6 Quantifying insightful problem solving: A modified compound remote associates paradigm using lexical priming to parametrically modulate different sources of task difficulty ... Becker, Maxi; Wiedemann, Gregor; Kuehn, Simone. - : PsyArXiv, 2018 BASE Show details 7 Modeling the dynamics of domain specific terminology in diachronic corpora ... Heyer, Gerhard; Kantner, Cathleen; Niekler, Andreas. - : arXiv, 2017 BASE Show details 8 Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis ... Niekler, Andreas; Wiedemann, Gregor; Heyer, Gerhard. - : arXiv, 2017 BASE Show details 9 Opening up to big data: computer-assisted analysis of textual data in social sciences Wiedemann, Gregor In: Historical Social Research ; 38 ; 4 ; 332-358 (2015) BASE Show details 10 Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis Niekler, Andreas; Wiedemann, Gregor; Heyer, Gerhard In: Terminology and Knowledge Engineering, TKE 2014 ; Terminology and Knowledge Engineering 2014 ; https://hal.archives-ouvertes.fr/hal-01005878 ; Terminology and Knowledge Engineering 2014, Jun 2014, Berlin, Germany. 10 p (2014) BASE Show details 11 Opening up to big data: computer-assisted analysis of textual data in social sciences Wiedemann, Gregor In: Forum Qualitative Sozialforschung / Forum: Qualitative Social Research ; 14 ; 2 ; 24 (2013) BASE Show details

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