Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish
Luise Dürlich | Sebastian Reimann | Gustav Finnveden | Joakim Nivre | Sara Stymne
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
Causality detection is the task of extracting information about causal relations from text. It is an important task for different types of document analysis, including political impact assessment. We present two new data sets for causality detection in Swedish. The first data set is annotated with binary relevance judgments, indicating whether a sentence contains causality information or not. In the second data set, sentence pairs are ranked for relevance with respect to a causality query, containing a specific hypothesized cause and/or effect. Both data sets are carefully curated and mainly intended for use as test data. We describe the data sets and their annotation, including detailed annotation guidelines. In addition, we present pilot experiments on cross-lingual zero-shot and few-shot causality detection, using training data from English and German.
Examining the Effects of Preprocessing on the Detection of Offensive Language in German Tweets
Sebastian Reimann | Daniel Dakota
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)