Subjects: Linguistics and Applied Linguistics >> Linguistics and Applied Linguistics submitted time 2022-01-05
Abstract： " [Objective] We study the reading performance of different fonts and writing systems that are using in Chinese publications. [Methods] Specifically, the Chinese characters in a sentence are rendered into their corresponding glyph images, then fold those images into a three-dimensional sentence tensor according to the word order. For different fonts or simplified/traditional Chinese text, we can get the corresponding representations with visual differences. By inputting the obtained sentence tensor into the proposed deep language model, we test them on text classification, which can objectively study the influence of font and writing system on reading performance. [Results] According to the experiments on two real-world Chinese text classification datasets, Toutiao and Thucnews, we found that the accuracy of text classification on some uncommon fonts is lower than that of common used fonts, and the text representation efficiency of different fonts in the common fonts is also different. [Conclusions] Through a hypothesis test, we found that there is a significant difference in the accuracy of using the data sets of regular script and bold script for text classification task, and the efficiency of regular script is higher than that of bold script. There are significant differences in reading performance between simplified and traditional writing systems. " "
Peer Review Status:
Awaiting Review
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