Technical gazette, Vol. 28 No. 1, 2021.
Original scientific paper
https://doi.org/10.17559/TV-20200914173747
Readability Assessment for Chinese L2 Sentences: An Extended Knowledge base and Comprehensive Evaluation Model-based Method
Shuqin Zhu*
; 1. Teacher's College, Beijing Union University, 5 Waiguanxiejie St, Beijing 100111, Peoples R China; 2. School of Artificial Intelligence, Beijing Normal University, 5 Waiguanxiejie St, Beijing 100111, Peoples R China
Jihua Song*
; School of Artificial Intelligence, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing, 100875, China
Weiming Peng
; School of Artificial Intelligence, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing, 100875, China
Jingbo Sun
; School of Artificial Intelligence, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing, 100875, China
Abstract
The paper assesses sentence readability based on the standards in the field of Chinese L2 teaching. In view of the inapplicability of the field standards in text readability assessment, the study focuses on two aspects. On the one hand, the graded lexicon of the HSK syllabus is extended to obtain a large-scale graded lexical knowledge base. On the other hand, sentence-based features in the existing teaching grammatical knowledge base are supplemented to achieve the automatic recognition of grammatical points and obtain quantitative grammatical indicators regarding sentence readability. Besides, based on the extended knowledge bases, comprehensive evaluation models are created to calculate the lexical and grammatical difficulties of sentences, as well as the sentence readability. The results of experiments show that the sentence readability is well differentiated in all levels of texts. Furthermore, the correlation between sentence readability and text readability is significantly improved in comparison with existing methods.
Keywords
Chinese L2 teaching; comprehensive evaluation model; extended knowledge base; sentence readability
Hrčak ID:
251552
URI
Publication date:
5.2.2021.
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