Automatic NLP-based enrichment of E-learning content for English language learning

Andreas Schulz, J. Lassig
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Abstract

The creation of quality content for E-learning resources is a time-consuming task. To simplify the process of content creation for language learning and enable easy adaptability for different requirements and language levels we strive to add as much automation as possible. In order to still obtain high quality, we present in this paper our approaches to enrich E-learning-based English vocabulary tests, which support blended learning and improve direct user feedback. We integrate openly available language resources for selecting and appending usage example sentences for a given vocabulary corpus. Furthermore we discuss our results and suggest to acquire natural language processing (NLP) based techniques to improve the generation of language related contents in general and to overcome some of the weaknesses of our current solution.
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基于nlp的英语在线学习内容自动丰富
为电子学习资源创建高质量的内容是一项耗时的任务。为了简化语言学习的内容创建过程,便于适应不同的要求和语言水平,我们努力增加尽可能多的自动化。为了保持高质量,我们在本文中提出了我们的方法来丰富基于e -learning的英语词汇测试,支持混合学习并改善直接用户反馈。我们整合了公开可用的语言资源,为给定的词汇语料库选择和附加用法例句。此外,我们讨论了我们的结果,并建议获得基于自然语言处理(NLP)的技术,以改善语言相关内容的生成,并克服我们当前解决方案的一些弱点。
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