Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia

E. Darío Gutiérrez, Guillermo Cecchi, Cheryl Corcoran, Philip Corlett


Abstract
The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures. However, previous research suggests there are disturbances in aspects of the language use of patients with schizophrenia. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier, that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.
Anthology ID:
D17-1316
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2923–2930
Language:
URL:
https://www.aclweb.org/anthology/D17-1316
DOI:
10.18653/v1/D17-1316
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1316.pdf
Video:
 https://vimeo.com/238236651