MultiAzterTest：一个用于语言可读性评估的多层次多语言分析器
MultiAzterTest: a Multilingual Analyzer on Multiple Levels of Language for Readability Assessment
摘要：
可读性评估是确定文本的难易程度或其等级的任务。传统上，依赖于语言的可读性公式已被使用，但这些公式很少考虑文本的特征。然而，评估文本复杂性的自然语言处理（NLP）工具能够测量更多不同的特征，并可以适应不同的语言。在本文中，我们提出了MultiAzterTest工具：（i）一个开源的NLP工具，它分析了超过125个英语，西班牙语和巴斯克语的内聚，语言和可读性措施的文本，但其架构旨在轻松适应其他语言;（ii）可读性评估分类器，提高了英语中的Coh-Meidong，西班牙语中的Coh-Meidong-Esp和巴斯克语中的ErreXail的性能; iii）网络工具。MultiAzterTest在使用SMO分类器将英语分类为三个阅读水平（初级，中级和高级）时获得90.09%的准确率，在巴斯克语中获得95.50%的准确率，在西班牙语中获得90%的准确率。使用跨语言功能，MultiAzterTest还获得了竞争力的结果，首先是在复杂与简单的区别。
英文摘要：
Readability assessment is the task of determining how difficult or easy a text is or which level/grade it has. Traditionally, language dependent readability formula have been used, but these formulae take few text characteristics into account. However, Natural Language Processing (NLP) tools that assess the complexity of texts are able to measure more different features and can be adapted to different languages. In this paper, we present the MultiAzterTest tool: (i) an open source NLP tool which analyzes texts on over 125 measures of cohesion,language, and readability for English, Spanish and Basque, but whose architecture is designed to easily adapt other languages; (ii) readability assessment classifiers that improve the performance of Coh-Metrix in English, Coh-Metrix-Esp in Spanish and ErreXail in Basque; iii) a web tool. MultiAzterTest obtains 90.09 % in accuracy when classifying into three reading levels (elementary, intermediate, and advanced) in English and 95.50 % in Basque and 90 % in Spanish when classifying into two reading levels (simple and complex) using a SMO classifier. Using cross-lingual features, MultiAzterTest also obtains competitive results above all in a complex vs simple distinction.
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。
