most citedProbing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction

7 citations · 8 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2020

Evaluating German Transformer Language Models with Syntactic Agreement Tests

Karolina Zaczynska, Nils Feldhus, Robert Schwarzenberg +2

Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most state-of-the-art NLP models now operate on top of TLMs to benefit f…

cs.CL20201 cited

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…

cs.CL20207 cited

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…

cs.CL2020

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…

cs.CL2020

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…