![](/images/graphics-bg.png)
User Adaptive Text Predictor for Mentally Disabled Huntington’s Patients
Joint Authors
Gelšvartas, Julius
Simutis, Rimvydas
Maskeliūnas, Rytis
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-23
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
This paper describes in detail the design of the specialized text predictor for patients with Huntington’s disease.
The main aim of the specialized text predictor is to improve the text input rate by limiting the phrases that the user can type in.
We show that such specialized predictor can significantly improve text input rate compared to a standard general purpose text predictor.
Specialized text predictor, however, makes it more difficult for the user to express his own ideas.
We further improved the text predictor by using the sematic database to extract synonym, hypernym, and hyponym terms for the words that are not present in the training data of the specialized text predictor.
This data can then be used to compute reasonable predictions for words that are originally not known to the text predictor.
American Psychological Association (APA)
Gelšvartas, Julius& Simutis, Rimvydas& Maskeliūnas, Rytis. 2016. User Adaptive Text Predictor for Mentally Disabled Huntington’s Patients. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1099636
Modern Language Association (MLA)
Gelšvartas, Julius…[et al.]. User Adaptive Text Predictor for Mentally Disabled Huntington’s Patients. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-6.
https://search.emarefa.net/detail/BIM-1099636
American Medical Association (AMA)
Gelšvartas, Julius& Simutis, Rimvydas& Maskeliūnas, Rytis. User Adaptive Text Predictor for Mentally Disabled Huntington’s Patients. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1099636
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099636