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
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2923–2930
- Language:
- URL:
- https://aclanthology.org/D17-1316
- DOI:
- 10.18653/v1/D17-1316
- Cite (ACL):
- E. Darío Gutiérrez, Guillermo Cecchi, Cheryl Corcoran, and Philip Corlett. 2017. Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2923–2930, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia (Gutiérrez et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D17-1316.pdf