Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading
Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading
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发表时间：
2016
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通讯作者：
U. Padó
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作者：
U. Padó
自动简答评分是帮助闭合大规模计算机化教育测试自动化循环的关键。迄今为止，已经提出了不同级别的语言处理的广泛特征。我们研究了一系列标准语料库中不同类型特征的相对重要性（包括英语和德语的语言技能和内容评估上下文）。我们发现词汇、文本相似性和依赖级别上的特征通常足以近似完整模型的性能。从语义处理中得出的特征特别有利于内容评估语料库中语言上更加多样化的答案。
Automated short-answer grading is key to help close the automation loop for large-scale, computerised testing in education. A wide range of features on different levels of linguistic processing has been proposed so far. We investigate the relative importance of the different types of features across a range of standard corpora (both from a language skill and content assessment context, in English and in German). We find that features on the lexical, text similarity and dependency level often suffice to approximate full-model performance. Features derived from semantic processing particularly benefit the linguistically more varied answers in content assessment corpora.