no code implementations • EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 • Jörg Wöckener, Thomas Haider, Tristan Miller, The-Khang Nguyen, Thanh Tung Linh Nguyen, Minh Vu Pham, Jonas Belouadi, Steffen Eger
In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone.
no code implementations • 30 Dec 2022 • Michael Zylla, Thomas Haider
Automated text analysis has become a widely used tool in political science.
no code implementations • 9 Mar 2022 • Georg Heiler, Thassilo Gadermaier, Thomas Haider, Allan Hanbury, Peter Filzmoser
Good quality network connectivity is ever more important.
1 code implementation • EACL 2021 • Thomas Haider
A prerequisite for the computational study of literature is the availability of properly digitized texts, ideally with reliable meta-data and ground-truth annotation.
1 code implementation • SEMEVAL 2020 • David Rother, Thomas Haider, Steffen Eger
Remarkably, with only 10 dimensional MBERT embeddings (reduced from the original size of 768), our submitted model performs best on subtask 1 for English and ranks third in subtask 2 for English.
1 code implementation • LREC 2020 • Thomas Haider, Steffen Eger, Evgeny Kim, Roman Klinger, Winfried Menninghaus
Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context.
1 code implementation • WS 2019 • Thomas Haider, Steffen Eger
Due to its semantic succinctness and novelty of expression, poetry is a great test bed for semantic change analysis.
no code implementations • COLING 2018 • Thomas Haider, Jonas Kuhn
We present the first supervised approach to rhyme detection with Siamese Recurrent Networks (SRN) that offer near perfect performance (97{\%} accuracy) with a single model on rhyme pairs for German, English and French, allowing future large scale analyses.
no code implementations • WS 2017 • Thomas Haider, Alexis Palmer
The feature sets are used for supervised text genre classification, on which our models achieve high accuracy.
no code implementations • LREC 2014 • Joachim Bingel, Thomas Haider
We describe a systematic and application-oriented approach to training and evaluating named entity recognition and classification (NERC) systems, the purpose of which is to identify an optimal system and to train an optimal model for named entity tagging DeReKo, a very large general-purpose corpus of contemporary German (Kupietz et al., 2010).