基于语言信息的文本复杂度分类：在ESL智能辅导中的应用
Text Complexity Classification Based on Linguistic Information: Application to Intelligent Tutoring of ESL
AI总结：
本研究构建了一个基于多维度语言特征的文本复杂度分类器，用于ESL阅读教学，实验表明其在原始语料上F分数达0.97，跨领域应用时性能下降但仍有实际可用性。
摘要：
这项工作的目标是构建一个分类器，能够在英语作为第二语言（ESL）学习者阅读教学的背景下识别文本复杂度。为了向语言学习者提供适合其英语水平的文本，我们识别了一组能够描述给定文本的音系、形态、词汇、句法、篇章和心理复杂性的特征。使用一个包含6171个文本的语料库（这些文本已由ESL专家分为三个不同难度级别），我们使用五种机器学习算法进行了不同实验。结果表明，所采用的语言特征提供了良好的整体分类性能（F分数=0.97）。我们进行了可扩展性评估，以测试此类分类器是否可用于实际应用中，例如可以集成到搜索引擎或网络抓取模块中。在此评估中，测试集中的文本不仅与训练集中的文本不同，而且类型也不同（ESL文本与儿童阅读文本）。尽管分类器的整体性能显著下降（F分数=0.65），但混淆矩阵显示，大多数分类错误发生在第二类和第三类（中间级别类别）之间，而系统在对第一类和第四类文本进行分类时具有稳健的性能。这种行为可以通过两个语料库之间分类标准的差异来解释。因此，观察到的结果证实了此类分类器在实际应用中的可用性。
英文摘要：
The goal of this work is to build a classifier that can identify text complexity within the context of teaching reading to English as a Second Language (ESL) learners. To present language learners with texts that are suitable to their level of English, a set of features that can describe the phonological, morphological, lexical, syntactic, discursive, and psychological complexity of a given text were identified. Using a corpus of 6171 texts, which had already been classified into three different levels of difficulty by ESL experts, different experiments were conducted with five machine learning algorithms. The results showed that the adopted linguistic features provide a good overall classification performance (F-Score = 0.97). A scalability evaluation was conducted to test if such a classifier could be used within real applications, where it can be, for example, plugged into a search engine or a web-scraping module. In this evaluation, the texts in the test set are not only different from those from the training set but also of different types (ESL texts vs. children reading texts). Although the overall performance of the classifier decreased significantly (F-Score = 0.65), the confusion matrix shows that most of the classification errors are between the classes two and three (the middle-level classes) and that the system has a robust performance in categorizing texts of class one and four. This behavior can be explained by the difference in classification criteria between the two corpora. Hence, the observed results confirm the usability of such a classifier within a real-world application.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
