Search Results for author:
Found 23 papers, 7 papers with code
Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate
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.
A Generalized Approach to Protest Event Detection in German Local News
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.
On Classifying whether Two Texts are on the Same Side of an Argument
To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed.
Boundary Detection and Categorization of Argument Aspects via Supervised Learning
This dataset enables both the supervised learning of boundaries and categorization of argument aspects.
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning
Few-shot learning and parameter-efficient fine-tuning (PEFT) are crucial to overcome the challenges of data scarcity and ever growing language model sizes.
Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine
In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods.
Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
Solidarity is a crucial concept to understand social relations in societies.
Application of the interactive Leipzig Corpus Miner as a generic research platform for the use in the social sciences
This article introduces to the interactive Leipzig Corpus Miner (iLCM) - a newly released, open-source software to perform automatic content analysis.
Forum 4.0: An Open-Source User Comment Analysis Framework
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.
UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection
Fine-tuning of pre-trained transformer networks such as BERT yield state-of-the-art results for text classification tasks.
Multi-modal Page Stream Segmentation with Convolutional Neural Networks
In the case of multi-page documents, the preservation of document contexts is a major requirement.
Optical Character Recognition
Optical Character Recognition (OCR)
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Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
Since vectors of the same word type can vary depending on the respective context, they implicitly provide a model for word sense disambiguation (WSD).
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
De-identification is the task of detecting protected health information (PHI) in medical text.
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection
We present a neural network based approach of transfer learning for offensive language detection.
microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF
We evaluate the performance of different word and character embeddings on two standard German datasets and with a special focus on out-of-vocabulary words.
Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter
Best results are achieved from pre-training our model on the unsupervised topic clustering of tweets in combination with thematic user cluster information.
A Multilingual Information Extraction Pipeline for Investigative Journalism
We introduce an advanced information extraction pipeline to automatically process very large collections of unstructured textual data for the purpose of investigative journalism.
New/s/leak 2.0 - Multilingual Information Extraction and Visualization for Investigative Journalism
Investigative journalism in recent years is confronted with two major challenges: 1) vast amounts of unstructured data originating from large text collections such as leaks or answers to Freedom of Information requests, and 2) multi-lingual data due to intensified global cooperation and communication in politics, business and civil society.
iLCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS).
Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features
In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Modeling the dynamics of domain specific terminology in diachronic corpora
In terminology work, natural language processing, and digital humanities, several studies address the analysis of variations in context and meaning of terms in order to detect semantic change and the evolution of terms.
Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis
This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis.
