Predicting Source Gaze Fixation Duration: A Machine Learning Approach

Tanik Saikh, Srinivas Bangalore, Michael Carl, Sivaji Bandyopadhyay

    Publikation: KonferencebidragPaperForskningpeer review

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    Abstrakt

    In this paper an attempt has been made to predict the gaze fixation duration at source text words using supervised learning method, namely Support Vector Machine. The machine learning models used in the present work make use of lexical, syntactic and semantic information for predicting the gaze fixation duration. Different features are extracted from the data and models are built by combining the features. Our best set up achieves close to 50% classification accuracy.

    OriginalsprogEngelsk
    Publikationsdato3 mar. 2015
    Antal sider6
    StatusUdgivet - 3 mar. 2015
    Begivenhed2015 International Conference on Cognitive Computing and Information Processing - JSS Academy of Technical Education, Noida, Indien
    Varighed: 3 mar. 20153 mar. 2015
    http://www.ccip.jssaten.ac.in/

    Konference

    Konference2015 International Conference on Cognitive Computing and Information Processing
    LokationJSS Academy of Technical Education
    LandIndien
    ByNoida
    Periode03/03/201503/03/2015
    SponsorJSS Academy of Technical Education, Noida
    Internetadresse

    Emneord

    • Eye Tracking
    • Gaze Fixation Duration
    • Machine learning
    • Support Vector Machine

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