Data-Driven Correction of FunctionWords in Non-Native English
Data-Driven Correction of FunctionWords in Non-Native English
复制标题
非母语英语虚词的数据驱动纠正
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Walt Detmar Meurers
中科院分区：
文献类型：
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作者：
Adriane Boyd;Walt Detmar Meurers
我们扩展了基于n-gram的数据驱动预测方法（Elghafari，Meurers和Wunsch，2010），以识别非母语学术文本中的功能词错误，作为帮助我们自己（HOO）共享任务的一部分。我们专注于四个类别的替代错误：介词，限定词，连词和量词。这些错误类型占HOO训练数据中注释的错误的12%。
在我们提交的最好的错误检测分数方面，我们检测到67%的介词和限定词替换错误，40%的连接词替换错误和33%的量词替换错误。对于检测到的大约一半的错误，我们也能够提供适当的校正。
We extend the n-gram-based data-driven prediction approach (Elghafari, Meurers and Wunsch, 2010) to identify function word errors in non-native academic texts as part of the Helping Our Own (HOO) Shared Task. We focus on substitution errors for four categories: prepositions, determiners, conjunctions, and quantifiers. These error types make up 12% of the errors annotated in the HOO training data.
In our best submission in terms of the error detection score, we detected 67% of preposition and determiner substitution errors, 40% of conjunction substitution errors, and 33% of quantifier substitution errors. For approximately half of the errors detected, we were also able to provide an appropriate correction.