Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation

Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation
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DOI:
10.34105/j.kmel.2010.02.016
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发表时间:
2010-08
期刊:
Knowledge Management & E-Learning: An International Journal
影响因子:
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通讯作者:
Takuya Goto;T. Kojiri;Toyohide Watanabe;Tomoharu Iwata;Takeshi Yamada
Takuya Goto;T. Kojiri;Toyohide Watanabe;Tomoharu Iwata;Takeshi Yamada
中科院分区:
其他
文献类型:
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作者:
Takuya Goto;T. Kojiri;Toyohide Watanabe;Tomoharu Iwata;Takeshi Yamada

文献摘要

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由于英语表达因体裁而异,因此学生学习从目标体裁的句子中产生的问题很重要。虽然准备了各种各样的问题,但仍然不足以满足学生想要学习的各种体裁。另一方面,在制作英语问题时,需要足够的语法知识和词汇,因此非专家很难自己准备英语问题。本文提出了一个基于英语文本的多项选择完形填空题自动生成系统。经验知识是产生适当问题所必需的,因此引入机器学习来从现有问题中获取知识。为了从文本中自动生成问题,系统(1)基于偏好学习从文本中提取适当的句子用于问题,(2)基于条件随机场估计空白部分,以及(3)基于现有问题的统计模式生成干扰项。实验结果表明,该方法在选择合适的句子和空白部分方面是可行的。此外,我们的方法是适当的,以产生可用的干扰,特别是对于不包含专有名词的句子。https://doi.org/10.34105/j.kmel.2010.02.016
Since English expressions vary according to the genres, it is important for students to study questions that are generated from sentences of the target genre. Although various questions are prepared, it is still not enough to satisfy various genres which students want to learn. On the other hand, when producing English questions, sufficient grammatical knowledge and vocabulary are needed, so it is difficult for non-expert to prepare English questions by themselves. In this paper, we propose an automatic generation system of multiple-choice cloze questions from English texts. Empirical knowledge is necessary to produce appropriate questions, so machine learning is introduced to acquire knowledge from existing questions. To generate the questions from texts automatically, the system (1) extracts appropriate sentences for questions from texts based on Preference Learning, (2) estimates a blank part based on Conditional Random Field, and (3) generates distracters based on statistical patterns of existing questions. Experimental results show our method is workable for selecting appropriate sentences and blank part. Moreover, our method is appropriate to generate the available distracters, especially for the sentence that does not contain the proper noun. https://doi.org/10.34105/j.kmel.2010.02.016