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        <title>Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia</title>
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        <namePart type="given">Dan</namePart>
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            <namePart type="given">Kate</namePart>
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    <abstract>Schizophrenia is a mental disorder which afflicts an estimated 0.7% of adults world wide. It affects many areas of mental function, often evident from incoherent speech. Diagnosing schizophrenia relies on subjective judgments resulting in disagreements even among trained clinicians. Recent studies have proposed the use of natural language processing for diagnosis by drawing on automatically-extracted linguistic features like discourse coherence and lexicon. Here, we present the first benchmark comparison of previously proposed coherence models for detecting symptoms of schizophrenia and evaluate their performance on a new dataset of recorded interviews between subjects and clinicians. We also present two alternative coherence metrics based on modern sentence embedding techniques that outperform the previous methods on our dataset. Lastly, we propose a novel computational model for reference incoherence based on ambiguous pronoun usage and show that it is a highly predictive feature on our data. While the number of subjects is limited in this pilot study, our results suggest new directions for diagnosing common symptoms of schizophrenia.</abstract>
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%0 Conference Proceedings
%T Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia
%A Iter, Dan
%A Yoon, Jong
%A Jurafsky, Dan
%Y Loveys, Kate
%Y Niederhoffer, Kate
%Y Prud’hommeaux, Emily
%Y Resnik, Rebecca
%Y Resnik, Philip
%S Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, LA
%F iter-etal-2018-automatic
%X Schizophrenia is a mental disorder which afflicts an estimated 0.7% of adults world wide. It affects many areas of mental function, often evident from incoherent speech. Diagnosing schizophrenia relies on subjective judgments resulting in disagreements even among trained clinicians. Recent studies have proposed the use of natural language processing for diagnosis by drawing on automatically-extracted linguistic features like discourse coherence and lexicon. Here, we present the first benchmark comparison of previously proposed coherence models for detecting symptoms of schizophrenia and evaluate their performance on a new dataset of recorded interviews between subjects and clinicians. We also present two alternative coherence metrics based on modern sentence embedding techniques that outperform the previous methods on our dataset. Lastly, we propose a novel computational model for reference incoherence based on ambiguous pronoun usage and show that it is a highly predictive feature on our data. While the number of subjects is limited in this pilot study, our results suggest new directions for diagnosing common symptoms of schizophrenia.
%R 10.18653/v1/W18-0615
%U https://aclanthology.org/W18-0615
%U https://doi.org/10.18653/v1/W18-0615
%P 136-146

Markdown (Informal)

[Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia](https://aclanthology.org/W18-0615) (Iter et al., CLPsych 2018)

• Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia (Iter et al., CLPsych 2018)

ACL

• Dan Iter, Jong Yoon, and Dan Jurafsky. 2018. Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pages 136–146, New Orleans, LA. Association for Computational Linguistics.