Using automated metaphor identification to aid in detection and prediction of first-episode schizophrenia

E. Darío Gutiérrez, Philip R. Corlett, Cheryl M. Corcoran, Guillermo A. Cecchi

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

33 Scopus citations

Abstract

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes many years and cannot be easily formalized into objective measures. However, clinical research suggests there are disturbances in aspects of the language use of patients with schizophrenia, which opens a door for the use of NLP tools in schizophrenia diagnosis and prognosis. 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.

Original languageEnglish
Title of host publicationEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages2923-2930
Number of pages8
ISBN (Electronic)9781945626838
DOIs
StatePublished - 2017
Externally publishedYes
Event2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017 - Copenhagen, Denmark
Duration: 9 Sep 201711 Sep 2017

Publication series

NameEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Country/TerritoryDenmark
CityCopenhagen
Period9/09/1711/09/17

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