122 citations · 181 across the 12 of their papers we have counts for
13 papers
Application of the interactive Leipzig Corpus Miner as a generic research platform for the use in the social sciences
Christian Kahmann, Andreas Niekler, Gregor Wiedemann
This article introduces to the interactive Leipzig Corpus Miner (iLCM) - a newly released, open-source software to perform automatic content analysis. Since the iLCM is based on th…
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
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 trai…
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
Gregor Wiedemann, Steffen Remus, Avi Chawla +1
Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in…
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
Max Friedrich, Arne Köhn, Gregor Wiedemann +1
De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHRs) to be shared for…
Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter
Gregor Wiedemann, Eugen Ruppert, Raghav Jindal +1
We investigate different strategies for automatic offensive language classification on German Twitter data. For this, we employ a sequentially combined BiLSTM-CNN neural network. B…
microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF
Gregor Wiedemann, Raghav Jindal, Chris Biemann
For named entity recognition (NER), bidirectional recurrent neural networks became the state-of-the-art technology in recent years. Competing approaches vary with respect to pre-tr…
A Multilingual Information Extraction Pipeline for Investigative Journalism
Gregor Wiedemann, Seid Muhie Yimam, Chris Biemann
We introduce an advanced information extraction pipeline to automatically process very large collections of unstructured textual data for the purpose of investigative journalism. T…
New/s/leak 2.0 - Multilingual Information Extraction and Visualization for Investigative Journalism
Gregor Wiedemann, Seid Muhie Yimam, Chris Biemann
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 answ…
iLCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
Andreas Niekler, Arnim Bleier, Christian Kahmann +5
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
Gregor Wiedemann, Gerhard Heyer
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. A…
Modeling the dynamics of domain specific terminology in diachronic corpora
Gerhard Heyer, Cathleen Kantner, Andreas Niekler +2
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 sema…
Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis
Andreas Niekler, Gregor Wiedemann, Gerhard Heyer
This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis. The infrastructure aims at the integration…
Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries
Gregor Wiedemann, Andreas Niekler
This paper presents a procedure to retrieve subsets of relevant documents from large text collections for Content Analysis, e.g. in social sciences. Document retrieval for this pur…