英文文本中的真词错误即输入的错词是和原词相似的另一个有效词.该文主要研究了对该类错误的检测.通过从所要检测的单词的上下文中提取句法和语义两个方面的特征,运用文档频率和信息增益进行特征筛选,实现了对上下文特征的有效提取.最终把判断该单词使用的正确与否看作分类问题,使用Winnow分类算法进行训练和测试.通过5阶交叉验证,所收集的61组混淆集的平均正确率与召回率分别为96%,79.47%.%Context dependent word errors in English texts are errors in which one is incorrectly substituted by another word without spelling mistake. This paper mainly discusses how to detect this type of errors. We prospose to extract the context festers by a method that firstly extract features of syntax and semantics from the word context, and then select the festures by the Document Frequency and the Information Gain to do feature selection.. Further, we model the detecting task as a classification using the Winnow algorithm. With five-across validation among 61groups of confusion sets, the average precision and recall are 96％, 79.47％, respectively.
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