Automatic Task Classification via Support Vector Machine and Crowdsourcing
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Telecommunications Engineering
Abstract EN
Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets.
Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public.
In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing.
To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform.
Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices.
Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%.
By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.
American Psychological Association (APA)
Shin, Hyungsik& Paek, Jeongyeup. 2018. Automatic Task Classification via Support Vector Machine and Crowdsourcing. Mobile Information Systems،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1204915
Modern Language Association (MLA)
Shin, Hyungsik& Paek, Jeongyeup. Automatic Task Classification via Support Vector Machine and Crowdsourcing. Mobile Information Systems No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1204915
American Medical Association (AMA)
Shin, Hyungsik& Paek, Jeongyeup. Automatic Task Classification via Support Vector Machine and Crowdsourcing. Mobile Information Systems. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1204915
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204915