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Identifying and Pruning Features for Classifying Translated and Post-edited Gaze Durations

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Mining Intelligence and Knowledge Exploration (MIKE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10089))

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Abstract

The present paper reports on various experiments carried out to classify the source and target gaze fixation durations on an eye tracking dataset, namely Translation Process Research (TPR). Different features were extracted from both the source and target parts of the TPR dataset, separately and different models were developed separately by employing such features using a machine learning framework. These models were trained using Support Vector Machine (SVM) and the best accuracy of 49.01% and 59.78% were obtained with respect to cross validation for source and target gaze fixation durations, respectively. The experiments were also carried out on the post edited data set using same experimental set up and the highest accuracy of 71.70% was obtained. Finally, Information Gain based pruning has been performed in order to select the best features that are useful for classifying the gaze durations.

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Notes

  1. 1.

    https://sites.google.com/site/centretranslationinnovation/translog-ii.

  2. 2.

    http://www.casmacat.eu/.

  3. 3.

    https://sites.google.com/site/centretranslationinnovation/tpr-db.

  4. 4.

    https://www.r-project.org/.

  5. 5.

    https://www.rstudio.com/products/rstudio/.

  6. 6.

    http://homes.cs.washington.edu/~shapiro/EE596/notes/InfoGain.pdf.

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Acknowledgements

The research work has received funding from the project “Development of Tree Bank in Indian Languages (TBIL)” funded by The Department of Electronics and Information Technology (DeitY), Ministry of Communication and Information Technology, Government of India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Tanik Saikh, Dipankar Das & Sivaji Bandyopadhayay

Authors
  1. Tanik Saikh
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  2. Dipankar Das
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  3. Sivaji Bandyopadhayay
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Correspondence to Sivaji Bandyopadhayay .

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Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Rajendra Prasath

  2. Center for Computing Research, CIC, National Polytechnic Institute, IPN, Mexico City, Distrito Federal, Mexico

    Alexander Gelbukh

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Saikh, T., Das, D., Bandyopadhayay, S. (2017). Identifying and Pruning Features for Classifying Translated and Post-edited Gaze Durations. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-58130-9_12

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  • Online ISBN: 978-3-319-58130-9

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