#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets

no code implementations • EMNLP (WNUT) 2020 • Hanna Varachkina, Stefan Ziehe, Tillmann Dönicke, Franziska Pannach

In this system paper, we present a transformer-based approach to the detection of informativeness in English tweets on the topic of the current COVID-19 pandemic.

Informativeness Task 2

Annotating Quantified Phenomena in Complex Sentence Structures Using the Example of Generalising Statements in Literary Texts

no code implementations • ACL (ISA, IWCS) 2021 • Tillmann Dönicke, Luisa Gödeke, Hanna Varachkina

We present a tagset for the annotation of quantification which we currently use to annotate certain quantified statements in fictional works of literature.

Sentence

A Unified Approach to Discourse Relation Classification in nine Languages

no code implementations • EMNLP (DISRPT) 2021 • Hanna Varachkina, Franziska Pannach

The intricate prediction task aims to predict a large number of classes from the Rhetorical Structure Theory (RST) framework for nine target languages.

Classification Relation +3

MONAPipe: Modes of Narration and Attribution Pipeline for German Computational Literary Studies and Language Analysis in spaCy

no code implementations • KONVENS (WS) 2022 • Tillman Dönicke, Florian Barth, Hanna Varachkina, Caroline Sporleder

Levels of Non-Fictionality in Fictional Texts

no code implementations • ISA (LREC) 2022 • Florian Barth, Hanna Varachkina, Tillmann Dönicke, Luisa Gödeke

In a quantitative experiment, we apply the entity-level fictionality tagger to our corpus and conclude that more non-fictional passages can be identified when information about real entities is available.