Marijn Schraagen


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Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media
Shihan Wang | Marijn Schraagen | Erik Tjong Kim Sang | Mehdi Dastani
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

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 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.


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Linguistic and Sociolinguistic Annotation of 17th Century Dutch Letters
Marijn Schraagen | Feike Dietz | Marjo van Koppen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Data-driven Morphology and Sociolinguistics for Early Modern Dutch
Marijn Schraagen | Marjo van Koppen | Feike Dietz
Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language


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Comparison between historical population archives and decentralized databases
Marijn Schraagen | Dionysius Huijsmans
Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities


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Evaluating Repetitions, or how to Improve your Multilingual ASR System by doing Nothing
Marijn Schraagen | Gerrit Bloothooft
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

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.