Adaptive Spelling Error Correction Models for Learner English
Adaptive Spelling Error Correction Models for Learner English
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DOI：
10.1016/j.procs.2017.08.065
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Ryo Nagata;Hiroya Takamura;Graham Neubig
中科院分区：
文献类型：
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作者：
Ryo Nagata;Hiroya Takamura;Graham Neubig
拼写错误是英语学习者的一个特征，它降低了针对英语学习者的自然语言处理系统的性能。本文描述了一种专门设计用于自动纠正学习者英语拼写错误的方法，该方法通过自适应地从原始学习者语料库中创建拼写错误纠正模型来减少噪音（例如语法和拼写错误）的影响。评价结果表明，该方法优于以往基于编辑距离和语言模型的方法。我们还报告了一项关于英语学习者倾向于犯哪些类型的拼写错误的调查结果，使用所提出的方法创建的拼写错误模型作为我们分析的工具。
Spelling errors are a characteristic of learner English and degrade the performances of natural language processing systems targeting English learners. This paper describes a method specially designed for automatically correcting spelling errors in learner English that reduces the effects from noise (e.g., grammatical and spelling errors) by adaptively creating spelling error correction models from raw learner corpora. An evaluation shows that the proposed method outperforms previous edit-distance-based and language-model-based methods. We also report results of an investigation into what types of spelling errors English learners tend to make, using the spelling error models created by the proposed method as a tool for our analysis.