Gregor Wiedemann


2022

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Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate
Lena Jurkschat | Gregor Wiedemann | Maximilian Heinrich | Mattes Ruckdeschel | Sunna Torge
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories. As an exemplary use case, we introduce the Argument Aspect Corpus - Nuclear Energy that separates arguments about the topic of nuclear energy into nine major aspects. Since the collection of training data for further aspects and topics is costly, we investigate the potential for current transformer-based few-shot learning approaches to accurately classify argument aspects. The best approach is applied to a British newspaper corpus covering the debate on nuclear energy over the past 21 years. Our evaluation shows that a stable prediction of shares of argument aspects in this debate is feasible with 50 to 100 training samples per aspect. Moreover, we see signals for a clear shift in the public discourse in favor of nuclear energy in recent years. This revelation of changing patterns of pro and contra arguments related to certain aspects over time demonstrates the potential of supervised argument aspect detection for tracking issue-specific media discourses.

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A Generalized Approach to Protest Event Detection in German Local News
Gregor Wiedemann | Jan Matti Dollbaum | Sebastian Haunss | Priska Daphi | Larissa Daria Meier
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Protest events provide information about social and political conflicts, the state of social cohesion and democratic conflict management, as well as the state of civil society in general. Social scientists are therefore interested in the systematic observation of protest events. With this paper, we release the first German language resource of protest event related article excerpts published in local news outlets. We use this dataset to train and evaluate transformer-based text classifiers to automatically detect relevant newspaper articles. Our best approach reaches a binary F1-score of 93.3 %, which is a promising result for our goal to support political science research. However, in a second experiment, we show that our model does not generalize equally well when applied to data from time periods and localities other than our training sample. To make protest event detection more robust, we test two ways of alternative preprocessing. First, we find that letting the classifier concentrate on sentences around protest keywords improves the F1-score for out-of-sample data up to +4 percentage points. Second, against our initial intuition, masking of named entities during preprocessing does not improve the generalization in terms of F1-scores. However, it leads to a significantly improved recall of the models.

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Boundary Detection and Categorization of Argument Aspects via Supervised Learning
Mattes Ruckdeschel | Gregor Wiedemann
Proceedings of the 9th Workshop on Argument Mining

Aspect-based argument mining (ABAM) is the task of automatic _detection_ and _categorization_ of argument aspects, i.e. the parts of an argumentative text that contain the issue-specific key rationale for its conclusion. From empirical data, overlapping but not congruent sets of aspect categories can be derived for different topics. So far, two supervised approaches to detect aspect boundaries, and a smaller number of unsupervised clustering approaches to categorize groups of similar aspects have been proposed. With this paper, we introduce the Argument Aspect Corpus (AAC) that contains token-level annotations of aspects in 3,547 argumentative sentences from three highly debated topics. This dataset enables both the supervised learning of boundaries and categorization of argument aspects. During the design of our annotation process, we noticed that it is not clear from the outset at which contextual unit aspects should be coded. We, thus, experiment with classification at the token, chunk, and sentence level granularity. Our finding is that the chunk level provides the most useful information for applications. At the same time, it produces the best performing results in our tested supervised learning setups.

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More Like This: Semantic Retrieval with Linguistic Information
Steffen Remus | Gregor Wiedemann | Saba Anwar | Fynn Petersen-Frey | Seid Muhie Yimam | Chris Biemann
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)

2021

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On Classifying whether Two Texts are on the Same Side of an Argument
Erik Körner | Gregor Wiedemann | Ahmad Dawar Hakimi | Gerhard Heyer | Martin Potthast
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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 considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.

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Forum 4.0: An Open-Source User Comment Analysis Framework
Marlo Haering | Jakob Smedegaard Andersen | Chris Biemann | Wiebke Loosen | Benjamin Milde | Tim Pietz | Christian Stöcker | Gregor Wiedemann | Olaf Zukunft | Walid Maalej
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

With the increasing number of user comments in diverse domains, including comments on online journalism and e-commerce websites, the manual content analysis of these comments becomes time-consuming and challenging. However, research showed that user comments contain useful information for different domain experts, which is thus worth finding and utilizing. This paper introduces Forum 4.0, an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. We demonstrate the applicability of Forum 4.0 with comments analytics scenarios within the domains of online journalism and app stores. We outline the underlying container architecture, including the web-based user interface, the machine learning component, and the task manager for time-consuming tasks. We finally conduct machine learning experiments with simulated annotations and different sampling strategies on existing datasets from both domains to evaluate Forum 4.0’s performance. Forum 4.0 achieves promising classification results (ROC-AUC ≥ 0.9 with 100 annotated samples), utilizing transformer-based embeddings with a lightweight logistic regression model. We explain how Forum 4.0’s architecture is applicable for millions of user comments in real-time, yet at feasible training and classification costs.

2020

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UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection
Gregor Wiedemann | Seid Muhie Yimam | Chris Biemann
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Fine-tuning of pre-trained transformer networks such as BERT yield state-of-the-art results for text classification tasks. Typically, fine-tuning is performed on task-specific training datasets in a supervised manner. One can also fine-tune in unsupervised manner beforehand by further pre-training the masked language modeling (MLM) task. Hereby, in-domain data for unsupervised MLM resembling the actual classification target dataset allows for domain adaptation of the model. In this paper, we compare current pre-trained transformer networks with and without MLM fine-tuning on their performance for offensive language detection. Our MLM fine-tuned RoBERTa-based classifier officially ranks 1st in the SemEval 2020 Shared Task 12 for the English language. Further experiments with the ALBERT model even surpass this result.

2019

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UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection
Gregor Wiedemann | Eugen Ruppert | Chris Biemann
Proceedings of the 13th International Workshop on Semantic Evaluation

We present a neural network based approach of transfer learning for offensive language detection. For our system, we compare two types of knowledge transfer: supervised and unsupervised pre-training. Supervised pre-training of our bidirectional GRU-3-CNN architecture is performed as multi-task learning of parallel training of five different tasks. The selected tasks are supervised classification problems from public NLP resources with some overlap to offensive language such as sentiment detection, emoji classification, and aggressive language classification. Unsupervised transfer learning is performed with a thematic clustering of 40M unlabeled tweets via LDA. Based on this dataset, pre-training is performed by predicting the main topic of a tweet. Results indicate that unsupervised transfer from large datasets performs slightly better than supervised training on small ‘near target category’ datasets. In the SemEval Task, our system ranks 14 out of 103 participants.

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Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
Max Friedrich | Arne Köhn | Gregor Wiedemann | Chris Biemann
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHR) to be shared for research. Automatic de-identification classifiers can significantly speed up the sanitization process. However, obtaining a large and diverse dataset to train such a classifier that works well across many types of medical text poses a challenge as privacy laws prohibit the sharing of raw medical records. We introduce a method to create privacy-preserving shareable representations of medical text (i.e. they contain no PHI) that does not require expensive manual pseudonymization. These representations can be shared between organizations to create unified datasets for training de-identification models. Our representation allows training a simple LSTM-CRF de-identification model to an F1 score of 97.4%, which is comparable to a strong baseline that exposes private information in its representation. A robust, widely available de-identification classifier based on our representation could potentially enable studies for which de-identification would otherwise be too costly.

2018

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A Multilingual Information Extraction Pipeline for Investigative Journalism
Gregor Wiedemann | Seid Muhie Yimam | Chris Biemann
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce an advanced information extraction pipeline to automatically process very large collections of unstructured textual data for the purpose of investigative journalism. The pipeline serves as a new input processor for the upcoming major release of our New/s/leak 2.0 software, which we develop in cooperation with a large German news organization. The use case is that journalists receive a large collection of files up to several Gigabytes containing unknown contents. Collections may originate either from official disclosures of documents, e.g. Freedom of Information Act requests, or unofficial data leaks.

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ILCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
Andreas Niekler | Arnim Bleier | Christian Kahmann | Lisa Posch | Gregor Wiedemann | Kenan Erdogan | Gerhard Heyer | Markus Strohmaier
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features
Gregor Wiedemann | Gerhard Heyer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)