Dynamic-Programming-Based Method for Fixation-to-Word Mapping

Eye movements made when reading text are considered to be important clues for estimating both understanding and interest. To analyze gaze data captured by the eye tracker with respect to a text, we need a noise-robust mapping between a fixation point and a word in the text. In this paper, we propose a dynamic-programming–based method for effective fixation-to-word mappings that can reduce the vertical displacement in gaze location. The golden dataset is created using FixFix, our web-based manual annotation tool. We first divide the gaze data into a number of sequential reading segments, then attempt to find the best segment-to-line alignment. To determine the best alignment, we select candidates for each segment, and calculate the cost based on the length characteristics of both the segment and document lines. We compare our method with the naive mapping method, and show that it is capable of producing more accurate fixation-to-word mappings.