Abstract
Through introducing the psychology model of reading cognitive, this paper uses the LSTM neural network and the CRF model respectively to simulate the language cognition process and the eye-movement control process in reading, in order to overcome the defect that the traditional CRF prediction model only considers the context information of the label sequence but can not take into account the context information of the text sequence. First, the psychological process of reading cognition is introduced and the prediction model of saccade target based on LSTM-CRF for Chinese reading is proposed. Then, the experimental data, experimental environment, feature templates and parameter settings needed for model training are introduced. Finally, the conclusion is drawn through experimental comparison: (1) The F1 score of prediction model in saccade labeling based on LSTM-CRF is superior to the traditional CRF prediction model; (2) The predictability of the language itself is an important feature of the saccade target prediction model; (3) The best saccade length for Chinese readers is about 2.5 Chinese characters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alkhateeb, J.H., Ren, J., Jiang, J., Ipson, S.S., Abed, H.E.: Word-based handwritten Arabic scripts recognition using DCT features and neural network classifier. In: 5th IEEE International Multi-Conference on Systems, Signals and Devices, pp. 517–530. IEEE Press, Amman (2008)
Bai, X.J., Yan, G.L., Li, X.: Eye movement control in Chinese reading: a summary over the past 20 years of research. Psychol. Dev. Educ. 31(1), 85–91 (2015)
Cop, U., Dirix, N., Drieghe, D., Duyck, W.: Presenting GECO: an eye-tracking corpus of monolingual and bilingual sentence reading. Behav. Res. Methods 49(2), 1–14 (2016)
Frisson, S., Harvey, D.R., Staub, A.: No prediction error cost in reading: evidence from eye movements. J. Mem. Lang. 95(4), 200–214 (2017)
Jiang, J., Trundle, P., Ren, J.: Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 34(8), 617–631 (2010)
Clifton Jr., C., Ferreira, F., Henderson, J.M., Inhoff, A.W., Liversedge, S.P.: Eye movements in reading and information processing: Keith Rayner’s 40 year legacy. J. Mem. Lang. 86(1), 1–19 (2016)
Kennedy, A., Pynte, J., Murray, W.S., Paul, S.A.: Frequency and predictability effects in the Dundee Corpus: an eye movement analysis. Q. J. Exp. Psychol. 66(3), 601–618 (2012)
Kuperberg, G.R., Jaeger, T.F.: What do we mean by prediction in language comprehension? Lang. Cognit. Neurosci. 31(1), 32–59 (2015)
Liu, Y., Reichle, E.D.: Eye-movement evidence for object-based attention in Chinese reading. Psychol. Sci. 29(2), 278–287 (2017)
Luke, S.G., Christianson, K.: Limits on lexical prediction during reading. Cogn. Psychol. 88(6), 22–60 (2016)
Luke, S.G., Christianson, K.: The Provo Corpus: a large eye-tracking corpus with predictability norms. Behav. Res. Methods 50(2), 826–833 (2018)
Moch, B.N., Komarudin, K., Susilo, M.S.: Development of eye fixation points prediction model from eye tracking data using neural network. Int. J. Technol. 8(6), 1082–1091 (2017)
Rayner, K., Li, X., Pollatsek, A.: Extending the E-Z Reader model of eye movement control to Chinese readers. Cognit. Sci. 31(6), 1021–1033 (2007)
Ren, J.: ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging. Knowl. Based Syst. 26(2), 144–153 (2012)
Ren, J., Wang, D., Jiang, J.: Effective recognition of MCCs in mammograms using an improved neural classifier. Eng. Appl. Artif. Intell. 24(4), 638–645 (2011)
Reichle, E.D.: Computational models of reading: a primer. Lang. Linguist. Compass 9(7), 271–284 (2015)
Sheridan, H., Reichle, E.D.: An analysis of the time course of lexical processing during reading. Cognit. Sci. 40(3), 522–553 (2015)
Slattery, T.J., Yates, M.: Word skipping: effects of word length, predictability, spelling and reading skill. Q. J. Exp. Psychol. 71(8), 1–30 (2017)
Su, H., Liu, Z.F., Cao, L.R.: The effects of word frequency and word predictability in preview and their implications for word segmentation in Chinese reading: evidence from eye movements. Acta Psychol. Sin. 48(6), 625–636 (2016)
Wang, Z., Ren, J., Zhang, D., Sun, M., Jiang, J.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287(2), 68–83 (2018)
Yu, L., Reichle, E.D.: Chinese versus English: insights on cognition during reading. Trends Cognit. Sci. 21(10), 721–724 (2017)
Reichle, E.D., Pollatsek, A., Fisher, D.L., Rayner, K.: Toward a model of eye movement control in reading. Psychol. Rev. 105(1), 125–157 (1998)
Engbert, R., Longtin, A., Kliegl, R.: A dynamical model of saccade generation in reading based on spatially distributed lexical processing. Vis. Res. 42(5), 621–636 (2002)
Acknowledgments
We would like to thank the associate editor and all of the reviewers for their constructive comments to improve the manuscript. The work is supported by NSF of China (Nos. NCYM0001) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 18YJCZH180).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Zhao, X., Xia, M. (2018). The Prediction Model of Saccade Target Based on LSTM-CRF for Chinese Reading. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-00563-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00562-7
Online ISBN: 978-3-030-00563-4
eBook Packages: Computer ScienceComputer Science (R0)