Luise Dürlich


2022

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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.

2018

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EFLLex: A Graded Lexical Resource for Learners of English as a Foreign Language
Luise Dürlich | Thomas François
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection
Luise Dürlich
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on “Irony detection in English tweets”. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.