54 citations · 68 across the 10 of their papers we have counts for
11 papers
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…