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Natural language processing (NLP) is becoming an important means forautomatic recognition of human traits and states, such as intoxication,presence of psychiatric disorders, presence of airway disorders and states ofstress. Such applications have the potential to be an important pillar foronline help lines, and may gradually be introduced into eHealth modules.However, NLP is language specific and for languages such as Dutch, NLP modelsare scarce. As a result, recent Dutch NLP models have a low capture of longrange semantic dependencies over sentences. To overcome this, here we presentbelabBERT, a new Dutch language model extending the RoBERTa architecture.belabBERT is trained on a large Dutch corpus (+32 GB) of web crawled texts. Weapplied belabBERT to the classification of psychiatric illnesses. First, weevaluated the strength of text-based classification using belabBERT, andcompared the results to the existing RobBERT model. Then, we compared theperformance of belabBERT to audio classification for psychiatric disorders.Finally, a brief exploration was performed, extending the framework to a hybridtext- and audio-based classification. Our results show that belabBERToutperformed the current best text classification network for Dutch, RobBERT.belabBERT also outperformed classification based on audio alone.

belabBERT: a Dutch RoBERTa-based language model applied to psychiatric classification