{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/text-based-classification-of-interviews-for","title":"Text-based classification of interviews for mental health -- juxtaposing the state of the art","arxiv_id":"2008.01543","date":"2020-07-29","proceeding":null,"authors":["Joppe Valentijn Wouts"],"abstract":"Currently, the state of the art for classification of psychiatric illness is based on audio-based classification. This thesis aims to design and evaluate a state of the art text classification network on this challenge. The hypothesis is that a well designed text-based approach poses a strong competition against the state-of-the-art audio based approaches. Dutch natural language models are being limited by the scarcity of pre-trained monolingual NLP models, as a result Dutch natural language models have a low capture of long range semantic dependencies over sentences. For this issue, this thesis presents belabBERT, a new Dutch language model extending the RoBERTa[15] architecture. belabBERT is trained on a large Dutch corpus (+32GB) of web crawled texts. After this thesis evaluates the strength of text-based classification, a brief exploration is done, extending the framework to a hybrid text- and audio-based classification. The goal of this hybrid framework is to show the principle of hybridisation with a very basic audio-classification network. The overall goal is to create the foundations for a hybrid psychiatric illness classification, by proving that the new text-based classification is already a strong stand-alone solution.","url_abs":"https://arxiv.org/abs/2008.01543v1","url_pdf":"https://arxiv.org/pdf/2008.01543v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"text-based-classification-of-interviews-for","repo_url":"https://github.com/Joppewouts/belabBERT","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"audio-classification","task_name":"Audio Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}