Search Results for author: Thomas Haider

Found 9 papers, 4 papers with code

End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?

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.

Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features

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.

CMCE at SemEval-2020 Task 1: Clustering on Manifolds of Contextualized Embeddings to Detect Historical Meaning Shifts

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.

Change Detection Word Embeddings

PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry

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.

Emotion Classification Emotion Recognition

Semantic Change and Emerging Tropes In a Large Corpus of New High German Poetry

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.

Supervised Rhyme Detection with Siamese Recurrent Networks

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.

General Classification

Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers

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).

Chunking Machine Translation +3

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