Papers by Gregor Wiedemann
Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates (2024.emnlp-main)
Copied to clipboard
| Challenge: | Traditionally, solidarity relied on common identity and reciprocity, potentially excluding out-groups like migrants. |
| Approach: | They examine the frequency of (anti-)solidarity towards women and migrants in German parliamentary debates between 1867 and 2022. |
| Outcome: | The proposed model outperforms other models in the analysis of 2,864 text snippets and finds that solidarity with migrants outweighs anti-solidarity but frequencies and solidarity types shift over time. |
A Multilingual Information Extraction Pipeline for Investigative Journalism (D18-2)
Copied to clipboard
| Challenge: | a new pipeline is being developed to process large collections of unstructured textual data . the pipeline is a key input processor for the upcoming major release of our software . |
| Approach: | a new pipeline is introduced to extract large amounts of unstructured data . the pipeline is used by journalists to process large files containing unknown contents . |
| Outcome: | the pipeline is an input processor for the upcoming major release of our new/s/leak 2.0 software. |
On Classifying whether Two Texts are on the Same Side of an Argument (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to same side stance classification (S3C) require domain knowledge and semantic inference to solve the task. |
| Approach: | They propose to use same side stance classification to predict whether two arguments argue for the same stance for a given pair of arguments. |
| Outcome: | The proposed model fails to generalize both within and across topics and domains when adjusting the sampling strategy to a more adversarial scenario. |
Forum 4.0: An Open-Source User Comment Analysis Framework (2021.eacl-demos)
Copied to clipboard
Marlo Haering, Jakob Smedegaard Andersen, Chris Biemann, Wiebke Loosen, Benjamin Milde, Tim Pietz, Christian Stöcker, Gregor Wiedemann, Olaf Zukunft, Walid Maalej
| Challenge: | Using Forum 4.0, we analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Approach: | They introduce an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
| Outcome: | The proposed framework can analyze, aggregate, and visualize user comments based on labels defined by domain experts. |
ILCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data (L18-1)
Copied to clipboard
Andreas Niekler, Arnim Bleier, Christian Kahmann, Lisa Posch, Gregor Wiedemann, Kenan Erdogan, Gerhard Heyer, Markus Strohmaier
| Challenge: | iLCM project develops integrated research environment for qualitative data analysis . text mining and text mining tools are extended by "Open Research Computing" |
| Approach: | iLCM project develops integrated research environment for analysis of structured and unstructured data in a "Software as a Service" architecture. |
| Outcome: | iLCM project develops integrated research environment for analysis of structured and unstructured data in a "Software as a Service" architecture. |
A Generalized Approach to Protest Event Detection in German Local News (2022.lrec-1)
Copied to clipboard
| Challenge: | Social scientists conduct protest event analysis to learn about developments and trends of the forms, scale and hot topics of political protests. |
| Approach: | They propose to use a German language resource to analyze newspaper articles on protest events . they train and evaluate transformer-based text classifiers to automatically detect relevant newspaper articles . |
| Outcome: | The proposed method achieves a binary F1-score of 93.3 %, but does not generalize well to other datasets. |
Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features (L18-1)
Copied to clipboard
| Challenge: | (retro-)digitizing paper-based files is a major undertaking for private and public archives and an important task in electronic mailroom applications. |
| Approach: | They propose to use convolutional neural networks to combine image and text features to achieve optimal document separation. |
| Outcome: | The proposed approach achieves an accuracy of 93 % and is considered a state-of-the-art for this task. |
Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to classify aspects of arguments are expensive and require training data for further aspects and topics. |
| Approach: | They propose a supervised aspect-based argument mining task to classify arguments into semantically coherent groups referring to the same defined aspect categories. |
| Outcome: | The proposed method is able to predict share of arguments in a British newspaper corpus with 50 to 100 examples per aspect. |
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records (P19-1)
Copied to clipboard
| Challenge: | De-identification is the task of detecting protected health information (PHI) in medical text. |
| Approach: | They propose to create shareable representations of medical text that contain no PHI and can be shared between organizations to create unified datasets for training de-identification models. |
| Outcome: | The proposed representation allows training a simple LSTM-CRF model to an F1 score of 97.4%. |