@inproceedings{sarioglu-kayi-etal-2017-predictive,
title = "Predictive Linguistic Features of Schizophrenia",
author = "Sarioglu Kayi, Efsun and
Diab, Mona and
Pauselli, Luca and
Compton, Michael and
Coppersmith, Glen",
editor = "Ide, Nancy and
Herbelot, Aur{\'e}lie and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-1028",
doi = "10.18653/v1/S17-1028",
pages = "241--250",
abstract = "Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.",
}
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<abstract>Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.</abstract>
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%0 Conference Proceedings
%T Predictive Linguistic Features of Schizophrenia
%A Sarioglu Kayi, Efsun
%A Diab, Mona
%A Pauselli, Luca
%A Compton, Michael
%A Coppersmith, Glen
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluís
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F sarioglu-kayi-etal-2017-predictive
%X Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.
%R 10.18653/v1/S17-1028
%U https://aclanthology.org/S17-1028
%U https://doi.org/10.18653/v1/S17-1028
%P 241-250
Markdown (Informal)
[Predictive Linguistic Features of Schizophrenia](https://aclanthology.org/S17-1028) (Sarioglu Kayi et al., *SEM 2017)
ACL
- Efsun Sarioglu Kayi, Mona Diab, Luca Pauselli, Michael Compton, and Glen Coppersmith. 2017. Predictive Linguistic Features of Schizophrenia. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 241–250, Vancouver, Canada. Association for Computational Linguistics.