Applying LDA topic modeling in communication research: Toward a valid and reliable methodology D Maier, A Waldherr, P Miltner, G Wiedemann, A Niekler, A Keinert, ... Computational methods for communication science, 13-38, 2021 | 725 | 2021 |
Opening up to Big Data: Computer-Assisted Analysis of Textual Data in Social Sciences G Wiedemann Historical Social Research 38 (4), 332-357, 2013 | 250 | 2013 |
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings G Wiedemann, S Remus, A Chawla, C Biemann 15th Conference on Natural Language Processing (KONVENS), 2019 | 217 | 2019 |
Text Mining for Qualitative Data Analysis in the Social Sciences. A Study on Democratic Discourse in Germany G Wiedemann Springer, 2016 | 156* | 2016 |
Text Mining in den Sozialwissenschaften. Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse M Lemke, G Wiedemann Springer, 2016 | 108* | 2016 |
Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter G Wiedemann, E Ruppert, R Jindal, C Biemann GermEval 2018@14th Conference on Natural Language Processing (KONVENS), 85-94, 2018 | 83 | 2018 |
UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection G Wiedemann, SM Yimam, C Biemann International Workshop on Semantic Evaluation (SemEval), 2020 | 58 | 2020 |
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records M Friedrich, A Köhn, G Wiedemann, C Biemann Association for Computational Linguistics (ACL), 5829-5839, 2019 | 44 | 2019 |
Postdemokratie und Neoliberalismus: zur Nutzung neoliberaler Argumentationen in der Bundesrepublik Deutschland 1949-2011; ein Werkstattbericht G Wiedemann, M Lemke, A Niekler ZPTh - Zeitschrift für Politische Theorie 4 (1), 99-115, 2013 | 44 | 2013 |
Topics and topical phases in German social media communication during a disaster S Gründer-Fahrer, A Schlaf, G Wiedemann, G Heyer Natural Language Engineering 24 (2), 221-264, 2018 | 43 | 2018 |
Leipzig Corpus Miner-A Text Mining Infrastructure for Qualitative Data Analysis A Niekler, G Wiedemann, G Heyer Terminology and Knowledge Engineering (TKE), 10, 2014 | 41 | 2014 |
Quantifying insightful problem solving: A modified compound remote associates paradigm using lexical priming to parametrically modulate different sources of task difficulty M Becker, G Wiedemann, S Kühn Psychological research 84 (2), 528-545, 2020 | 39 | 2020 |
Proportional classification revisited: Automatic content analysis of political manifestos using active learning G Wiedemann Social Science Computer Review 37 (2), 135-159, 2019 | 37 | 2019 |
How document sampling and vocabulary pruning affect the results of topic models D Maier, A Niekler, G Wiedemann, D Stoltenberg Computational Communication Research 2 (2), 139-152, 2020 | 32 | 2020 |
Content Analysis between Quality and Quantity M Lemke, A Niekler, GS Schaal, G Wiedemann Datenbank-Spektrum 15 (1), 7-14, 2015 | 32 | 2015 |
Analyse qualitativer Daten mit dem "Leipzig Corpus Miner" G Wiedemann, A Niekler Text Mining in den Sozialwissenschaften, 63-88, 2016 | 28 | 2016 |
Hands-On: A Five Day Text Mining Course for Humanists and Social Scientists in R G Wiedemann, A Niekler Workshop on Teaching NLP for Digital Humanities (Teach4DH@GSCL) 2017, 57-65, 2017 | 27 | 2017 |
Multi-modal page stream segmentation with convolutional neural networks G Wiedemann, G Heyer Language Resources and Evaluation 55 (1), 127-150, 2021 | 16 | 2021 |
New/s/leak 2.0–Multilingual Information Extraction and Visualization for Investigative Journalism G Wiedemann, SM Yimam, C Biemann International Conference on Social Informatics (SocInfo), 313-322, 2018 | 16 | 2018 |
Text Mining für die Analyse qualitativer Daten. Auf dem Weg zu einer Best Practice? G Wiedemann, M Lemke Text Mining in den Sozialwissenschaften, 397-419, 2016 | 16 | 2016 |