Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationCLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings
EditorsAyah Zirikly, Dana Atzil-Slonim, Maria Liakata, Steven Bedrick, Bart Desmet, Molly Ireland, Andrew Lee, Sean MacAvaney, Matthew Purver, Rebecca Resnik, Andrew Yates
PublisherAssociation for Computational Linguistics (ACL)
Pages148-157
Number of pages10
ISBN (Electronic)9781955917872
StatePublished - 2022
Event8th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2022 - Seattle, United States
Duration: 15 Jul 2022 → …

Publication series

NameCLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings

Conference

Conference8th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2022
Country/TerritoryUnited States
CitySeattle
Period15/07/22 → …

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