Michael Wojatzki


2019

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ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter
Huangpan Zhang | Michael Wojatzki | Tobias Horsmann | Torsten Zesch
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.

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LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness
Piush Aggarwal | Tobias Horsmann | Michael Wojatzki | Torsten Zesch
Proceedings of the 13th International Workshop on Semantic Evaluation

We present results for Subtask A and C of SemEval 2019 Shared Task 6. In Subtask A, we experiment with an embedding representation of postings and use BERT to categorize postings. Our best result reaches the 10th place (out of 103). In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).

2018

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Agree or Disagree: Predicting Judgments on Nuanced Assertions
Michael Wojatzki | Torsten Zesch | Saif Mohammad | Svetlana Kiritchenko
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.

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Quantifying Qualitative Data for Understanding Controversial Issues
Michael Wojatzki | Saif Mohammad | Torsten Zesch | Svetlana Kiritchenko
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Bundled Gap Filling: A New Paradigm for Unambiguous Cloze Exercises
Michael Wojatzki | Oren Melamud | Torsten Zesch
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Validating bundled gap filling – Empirical evidence for ambiguity reduction and language proficiency testing capabilities
Niklas Meyer | Michael Wojatzki | Torsten Zesch
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

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ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers
Michael Wojatzki | Torsten Zesch
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Task-Independent Features for Automated Essay Grading
Torsten Zesch | Michael Wojatzki | Dirk Scholten-Akoun
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications