54 citations · 73 across the 12 of their papers we have counts for
13 papers
PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction
Arne Binder, Leonhard Hennig, Christoph Alt
The objective of Information Extraction (IE) is to derive structured representations from unstructured or semi-structured documents. However, developing IE models is complex due to…
A Medical Information Extraction Workbench to Process German Clinical Text
Roland Roller, Laura Seiffe, Ammer Ayach +9
Background: In the information extraction and natural language processing domain, accessible datasets are crucial to reproduce and compare results. Publicly available implementatio…
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-…
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…
Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
David Harbecke, Christoph Alt
Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occl…
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…
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…
Learning Explanations from Language Data
David Harbecke, Robert Schwarzenberg, Christoph Alt
PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks. We demonstrate that it also generates meaningful inte…