Prescriptive grammar rules are taught in education, generally to ban the use of certain frequently encountered constructions in everyday language. This may lead to hypercorrection, meaning that the prescribed form in one construction is extended to another one in which it is in fact prohibited by prescriptive grammar. We discuss two such cases in Dutch: the hypercorrect use of the comparative particle dan ‘than’ in equative constructions, and the hypercorrect use of the accusative pronoun hen ‘them’ for a dative object. In two experiments, high school students of three educational levels were tested on their use of these hypercorrect forms (nexp1 = 162, nexp2 = 159). Our results indicate an overall large amount of hypercorrection across all levels of education, including pre-university level students who otherwise perform better in constructions targeted by prescriptive grammar rules. We conclude that while teaching prescriptive grammar rules to high school students seems to increase their use of correct forms in certain constructions, this comes at a cost of hypercorrection in others.
Diagnosing mental disorders is complex due to the genetic, environmental and psychological contributors and the individual risk factors. Language markers for mental disorders can help to diagnose a person. Research thus far on language markers and the associated mental disorders has been done mainly with the Linguistic Inquiry and Word Count (LIWC) program. In order to improve on this research, we employed a range of Natural Language Processing (NLP) techniques using LIWC, spaCy, fastText and RobBERT to analyse Dutch psychiatric interview transcriptions with both rule-based and vector-based approaches. Our primary objective was to predict whether a patient had been diagnosed with a mental disorder, and if so, the specific mental disorder type. Furthermore, the second goal of this research was to find out which words are language markers for which mental disorder. LIWC in combination with the random forest classification algorithm performed best in predicting whether a person had a mental disorder or not (accuracy: 0.952; Cohen’s kappa: 0.889). SpaCy in combination with random forest predicted best which particular mental disorder a patient had been diagnosed with (accuracy: 0.429; Cohen’s kappa: 0.304).
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