首页> 外文会议>Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies >Automatic Generation of Context-Based Fill-in-the-Blank Exercises Using Co-occurrence Likelihoods and Google n-grams
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Automatic Generation of Context-Based Fill-in-the-Blank Exercises Using Co-occurrence Likelihoods and Google n-grams

机译:使用共同发生的似然和谷歌n-gram自动生成基于上下背景的填空练习

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In this paper, we propose a method of automatically generating multiple-choice fill-in-the-blank exercises from existing text passages that challenge a reader's comprehension skills and contextual awareness. We use a unique application of word co-occurrence likelihoods and the Google n-grams corpus to select words with strong contextual links to their surrounding text, and to generate distrac-tors that make sense only in an isolated narrow context and not in the full context of the passage. Results show that our method is successful at generating questions with distrac-tors that are semantically consistent in a narrow context but inconsistent given the full text, with larger n-grams yielding significantly better results.
机译:在本文中,我们提出了一种从现有文本段落中自动生成多项选择填空练习的方法,这些段落挑战读者的理解技能和背景意识。我们使用独特的单词共同发生似然和谷歌n-grams语料库来选择与周围文本的强大上下文链接的单词,并生成仅在孤立的狭窄背景下有意义的分解,而不是完整通道的背景。结果表明,我们的方法是成功的,在发电问题中,在狭隘的上下文中的语义一致,但给出了全文的不一致,较大的n-gram产生明显更好的结果。

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