54 citations · 84 across the 18 of their papers we have counts for
20 papers
Factuality Detection using Machine Translation -- a Use Case for German Clinical Text
Mohammed Bin Sumait, Aleksandra Gabryszak, Leonhard Hennig +1
Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not…
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
Leonhard Hennig, Philippe Thomas, Sebastian Möller
Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable…
Multilingual Relation Classification via Efficient and Effective Prompting
Yuxuan Chen, David Harbecke, Leonhard Hennig
Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual sett…
Full-Text Argumentation Mining on Scientific Publications
Arne Binder, Bhuvanesh Verma, Leonhard Hennig
Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two…
Confidence estimation of classification based on the distribution of the neural network output layer
Abdel Aziz Taha, Leonhard Hennig, Petr Knoth
One of the most common problems preventing the application of prediction models in the real world is lack of generalization: The accuracy of models, measured in the benchmark does…
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Nils Feldhus, Leonhard Hennig, Maximilian Dustin Nasert +3
Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many fe…
Why only Micro-F1? Class Weighting of Measures for Relation Classification
David Harbecke, Yuxuan Chen, Leonhard Hennig +1
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro…
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
Yuxuan Chen, Jonas Mikkelsen, Arne Binder +2
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-…
MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain
Leonhard Hennig, Phuc Tran Truong, Aleksandra Gabryszak
We present MobIE, a German-language dataset, which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entit…
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
Marc Hübner, Christoph Alt, Robert Schwarzenberg +1
Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Ta…
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Hanchu Zhang, Leonhard Hennig, Christoph Alt +3
Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets. Recognizing novel entity types in this domain, such as p…
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task
Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig
TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training a…
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig
Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, cond…
SIA: A Scalable Interoperable Annotation Server for Biomedical Named Entities
Johannes Kirschnick, Philippe Thomas, Roland Roller +1
Recent years showed a strong increase in biomedical sciences and an inherent increase in publication volume. Extraction of specific information from these sources requires highly s…
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products
Saskia Schön, Veselina Mironova, Aleksandra Gabryszak +1
Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as su…
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
Martin Schiersch, Veselina Mironova, Maximilian Schmitt +3
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific c…
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
Dmitrii Aksenov, Julián Moreno-Schneider, Peter Bourgonje +3
We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with condi…
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke +2
Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolution…
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Christoph Alt, Marc Hübner, Leonhard Hennig
Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate t…
Improving Relation Extraction by Pre-trained Language Representations
Christoph Alt, Marc Hübner, Leonhard Hennig
Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training fe…