Christian Wolff


2021

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Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary Language
Thomas Schmidt | Katrin Dennerlein | Christian Wolff
Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

We present results of a project on emotion classification on historical German plays of Enlightenment, Storm and Stress, and German Classicism. We have developed a hierarchical annotation scheme consisting of 13 sub-emotions like suffering, love and joy that sum up to 6 main and 2 polarity classes (positive/negative). We have conducted textual annotations on 11 German plays and have acquired over 13,000 emotion annotations by two annotators per play. We have evaluated multiple traditional machine learning approaches as well as transformer-based models pretrained on historical and contemporary language for a single-label text sequence emotion classification for the different emotion categories. The evaluation is carried out on three different instances of the corpus: (1) taking all annotations, (2) filtering overlapping annotations by annotators, (3) applying a heuristic for speech-based analysis. Best results are achieved on the filtered corpus with the best models being large transformer-based models pretrained on contemporary German language. For the polarity classification accuracies of up to 90% are achieved. The accuracies become lower for settings with a higher number of classes, achieving 66% for 13 sub-emotions. Further pretraining of a historical model with a corpus of dramatic texts led to no improvements.

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Lexicon-based Sentiment Analysis in German: Systematic Evaluation of Resources and Preprocessing Techniques
Jakob Fehle | Thomas Schmidt | Christian Wolff
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

2016

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Creating a Lexicon of Bavarian Dialect by Means of Facebook Language Data and Crowdsourcing
Manuel Burghardt | Daniel Granvogl | Christian Wolff
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Data acquisition in dialectology is typically a tedious task, as dialect samples of spoken language have to be collected via questionnaires or interviews. In this article, we suggest to use the “web as a corpus” approach for dialectology. We present a case study that demonstrates how authentic language data for the Bavarian dialect (ISO 639-3:bar) can be collected automatically from the social network Facebook. We also show that Facebook can be used effectively as a crowdsourcing platform, where users are willing to translate dialect words collaboratively in order to create a common lexicon of their Bavarian dialect. Key insights from the case study are summarized as “lessons learned”, together with suggestions for future enhancements of the lexicon creation approach.

2004

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Linguistic Corpus Search
Christian Biemann | Uwe Quasthoff | Christian Wolff
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Web Services for Language Resources and Language Technology Applications
Christian Biemann | Stefan Bordag | Uwe Quasthoff | Christian Wolff
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2002

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Information Extraction from Text Corpora: Using Filters on Collocation Sets
Gerhard Heyer | Uwe Quasthoff | Christian Wolff
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Named Entity Learning and Verification: Expectation Maximization in Large Corpora
Uwe Quasthoff | Christian Biemann | Christian Wolff
COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002)

2000

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A Flexible Infrastructure for Large Monolingual Corpora
Uwe Quasthoff | Christian Wolff
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)