How lies can help us understand: The role of deception in discourse modelling

This thesis considers multiple definitions of deception and tests Gricean pragmatics and Austin's Speech Act Theory on their ability to account for other forms of deception besides false assertion, namely false implicature and presupposition faking. It also analyses the ability of several representations of the common ground to account for deception. It shows that Grice's maxim of quality and Searle's sincerity condition are not adequately defined, and that multiple representations of the common ground do not make explicit which belief sets can enter the common ground when one interlocutor deceives another

Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients

© European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level (F1-score above 80% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning