Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis

Yaara Shriki, Ido Ziv, Nachum Dershowitz, Eiran Harel, Kfir Bar


Abstract
Natural language processing tools have been shown to be effective for detecting symptoms of schizophrenia in transcribed speech. We analyze and assess the contribution of the various syntactic and morphological categories towards successful machine classification of texts produced by subjects with schizophrenia and by others. Specifically, we fine-tune a language model for the classification task, and mask all words that are attributed with each category of interest. The speech samples were generated in a controlled way by interviewing inpatients who were officially diagnosed with schizophrenia, and a corresponding group of healthy controls. All participants are native Hebrew speakers. Our results show that nouns are the most significant category for classification performance.
Anthology ID:
2022.clpsych-1.13
Volume:
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
Month:
July
Year:
2022
Address:
Seattle, USA
Venues:
CLPsych | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–157
Language:
URL:
https://aclanthology.org/2022.clpsych-1.13
DOI:
10.18653/v1/2022.clpsych-1.13
Bibkey:
Cite (ACL):
Yaara Shriki, Ido Ziv, Nachum Dershowitz, Eiran Harel, and Kfir Bar. 2022. Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis. In Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology, pages 148–157, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis (Shriki et al., CLPsych 2022)
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PDF:
https://aclanthology.org/2022.clpsych-1.13.pdf