@inproceedings{shriki-etal-2022-masking,
title = "Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis",
author = "Shriki, Yaara and
Ziv, Ido and
Dershowitz, Nachum and
Harel, Eiran and
Bar, Kfir",
editor = "Zirikly, Ayah and
Atzil-Slonim, Dana and
Liakata, Maria and
Bedrick, Steven and
Desmet, Bart and
Ireland, Molly and
Lee, Andrew and
MacAvaney, Sean and
Purver, Matthew and
Resnik, Rebecca and
Yates, Andrew",
booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
month = jul,
year = "2022",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.clpsych-1.13/",
doi = "10.18653/v1/2022.clpsych-1.13",
pages = "148--157",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis
%A Shriki, Yaara
%A Ziv, Ido
%A Dershowitz, Nachum
%A Harel, Eiran
%A Bar, Kfir
%Y Zirikly, Ayah
%Y Atzil-Slonim, Dana
%Y Liakata, Maria
%Y Bedrick, Steven
%Y Desmet, Bart
%Y Ireland, Molly
%Y Lee, Andrew
%Y MacAvaney, Sean
%Y Purver, Matthew
%Y Resnik, Rebecca
%Y Yates, Andrew
%S Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F shriki-etal-2022-masking
%X 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.
%R 10.18653/v1/2022.clpsych-1.13
%U https://aclanthology.org/2022.clpsych-1.13/
%U https://doi.org/10.18653/v1/2022.clpsych-1.13
%P 148-157
Markdown (Informal)
[Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis](https://aclanthology.org/2022.clpsych-1.13/) (Shriki et al., CLPsych 2022)
ACL