论文标题
LXPER索引：针对韩国EFL学生的课程特定文本可读性评估模型
LXPER Index: a curriculum-specific text readability assessment model for EFL students in Korea
论文作者
论文摘要
自动可读性评估是自然语言处理（NLP）在教育中最重要的应用之一。由于自动可读性评估允许在各个熟练程度上为读者快速选择适当的阅读材料，因此它对于全球英语作为外语（EFL）学生的英语教育特别有用。大多数可读性评估模型都是为英语的本地读者开发的，并且在非母语英语培训（ELT）课程中的文本准确性较低。我们介绍了LXPER指数，这是韩国ELT课程中非本地EFL读者的可读性评估模型。我们的实验表明，我们的新模型接受了COKEC-TEXT（韩国ELT课程的文本语料库）培训，可显着提高韩国ELT课程中文本的自动可读性评估的准确性。
Automatic readability assessment is one of the most important applications of Natural Language Processing (NLP) in education. Since automatic readability assessment allows the fast selection of appropriate reading material for readers at all levels of proficiency, it can be particularly useful for the English education of English as Foreign Language (EFL) students around the world. Most readability assessment models are developed for the native readers of English and have low accuracy for texts in the non-native English Language Training (ELT) curriculum. We introduce LXPER Index, which is a readability assessment model for non-native EFL readers in the ELT curriculum of Korea. Our experiments show that our new model, trained with CoKEC-text (Text Corpus of the Korean ELT Curriculum), significantly improves the accuracy of automatic readability assessment for texts in the Korean ELT curriculum.
