Wiebke Petersen


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hhuEDOS at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) Binary Sexism Detection (Subtask A)
Wiebke Petersen | Diem-Ly Tran | Marion Wroblewitz
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper, we describe SemEval-2023 Task 10, a shared task on detecting and predicting sexist language. The dataset consists of labeled sexist and non-sexist data targeted towards women acquired from both Reddit and Gab. We present and compare several approaches we experimented with and our final submitted model. Additional error analysis is given to recognize challenges we dealt with in our process. A total of 84 teams participated. Our model ranks 55th overall in Subtask A of the shared task.


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HHUplexity at Text Complexity DE Challenge 2022
David Arps | Jan Kels | Florian Krämer | Yunus Renz | Regina Stodden | Wiebke Petersen
Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text

In this paper, we describe our submission to the ‘Text Complexity DE Challenge 2022’ shared task on predicting the complexity of German sentences. We compare performance of different feature-based regression architectures and transformer language models. Our best candidate is a fine-tuned German Distilbert model that ignores linguistic features of the sentences. Our model ranks 7th place in the shared task.


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AET: Web-based Adjective Exploration Tool for German
Tatiana Bladier | Esther Seyffarth | Oliver Hellwig | Wiebke Petersen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Unsupervised Induction of Compositional Types for English Adjective-Noun Pairs
Wiebke Petersen | Oliver Hellwig
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Short papers


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Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German
Wiebke Petersen | Oliver Hellwig
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.