Being able to obtain timely information about an event, like a protest, becomes increasingly more relevant with the rise of affective polarisation and social unrest over the world. Nowadays, large-scale protests tend to be organised and broadcast through social media. Analysing social media platforms like X has proven to be an effective method to follow events during a protest. Thus, we trained several language models on Dutch tweets to analyse their ability to classify if a tweet expresses discontent, considering these tweets may contain practical information about a protest. Our results show that models pre-trained on Twitter data, including Bernice and TwHIN-BERT, outperform models that are not. Additionally, the results showed that Sentence Transformers is a promising model. The added value of oversampling is greater for models that were not trained on Twitter data. In line with previous work, pre-processing the data did not help a transformer language model to make better predictions.

Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV)”:” Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training. However, a good performance on the AV task does not ensure good “general-purpose” style representations. For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone. We introduce a variation of the AV training task that controls for content using conversation or domain labels. We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework. We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content.

With the legal sector embracing digitization, the increasing availability of information has led to a need for systems that can automatically summarize legal documents. Most existing research on legal text summarization has so far focused on extractive models, which can result in awkward summaries, as sentences in legal documents can be very long and detailed. In this study, we apply two abstractive summarization models on a Dutch legal domain dataset. The results show that existing models transfer quite well across domains and languages: the ROUGE scores of our experiments are comparable to state-of-the-art studies on English news article texts. Examining one of the models showed the capability of rewriting long legal sentences to much shorter ones, using mostly vocabulary from the source document. Human evaluation shows that for both models hand-made summaries are still perceived as more relevant and readable, and automatic summaries do not always capture elements such as background, considerations and judgement. Still, generated summaries are valuable if only a keyword summary or no summary at all is present.

Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.

This paper presents a method to compute similarity of folktales based on conceptual overlap at various levels of abstraction as defined in Dutch WordNet. The method is applied on a corpus of Dutch folktales and evaluated using a comparison to traditional folktale similarity analysis based on the Aarne–Thompson–Uther (ATU) classification system. Document similarity computed by the presented method is in agreement with traditional analysis for a certain amount of folktale pairs, but differs for other pairs. However, it can be argued that the current approach computes an alternative, data-driven type of similarity. Using WordNet instead of a domain-specific ontology or classification system ensures applicability of the method outside of the folktale domain.

Repetition is a common concept in human communication. This paper investigates possible benefits of repetition for automatic speech recognition under controlled conditions. Testing is performed on the newly created Autonomata TOO speech corpus, consisting of multilingual names for Points-Of-Interest as spoken by both native and non-native speakers. During corpus recording, ASR was being performed under baseline conditions using a Nuance Vocon 3200 system. On failed recognition, additional attempts for the same utterances were added to the corpus. Substantial improvements in recognition results are shown for all categories of speakers and utterances, even if speakers did not noticeably alter their previously misrecognized pronunciation. A categorization is proposed for various types of differences between utterance realisations. The number of attempts, the pronunciation of an utterance over multiple attempts compared to both previous attempts and reference pronunciation is analyzed for difference type and frequency. Variables such as the native language of the speaker and the languages in the lexicon are taken into account. Possible implications for ASR research are discussed.