The Prediction Model of Saccade Target Based on LSTM-CRF for Chinese Reading

摘要

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.