摘要
Selecting appropriate texts for L2 (second/foreign language) learners is an important approach to enhancing motivation and, by extension, learning. There is currently no tool for classifying foreign language texts according to a language proficiency framework, which makes it difficult for students and educators to determine the precise difficulty/complexity levels of an unclassified text. Taking the Chinese language as an example, this study aimed to create a readability assessment system, called the Chinese Readability Index Explorer for Chinese as a Foreign Language (CRIE-CFL), in order to level-that is, to sort by proficiency level-texts that will be used for instructional purposes. The framework of choice in this project is the Common European Framework of Reference (CEFR). A team of expert CFL teachers first classified 1,578 CFL texts into their appropriate CEFR levels. A set of 30 CFL readability features was then developed or drawn from previous research, and sorted according to importance using F-scores. In addition, a support vector machine model was trained by sequentially integrating the features into the model to optimize accuracy. The empirical evaluation of CRIE-CFL revealed average exact- and adjacent-level accuracies of 74.97% and 99.62%, respectively, for predicting the expert classification of a text. The functionalities of CRIE-CFL are introduced and discussed.
原文 | 英語 |
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頁(從 - 到) | 371-391 |
頁數 | 21 |
期刊 | Modern Language Journal |
卷 | 99 |
發行號 | 2 |
DOIs | |
出版狀態 | 已發佈 - 2015 6月 1 |
ASJC Scopus subject areas
- 語言與語言學
- 語言和語言學