An Unsupervised Approach to Preposition Error Correction

摘要：

In this work, an unsupervised statistical method for automatic correction of preposition errors using the Google n-gram data set is presented and com- pared to the state-of-the-art. We use the Google n- gram data set in a back-o fashion that increases the performance of the method. The method works au- tomatically, does not require any human-annotated knowledge resources (e.g., ontologies) and can be ap- plied to English language texts, including non-native (L2) ones in which preposition errors are known to be numerous. The method can be applied to other languages for which Google n-grams are available.

关键词： Preposition errors Google web 1T n-grams

作者: Aminul ISLAM Diana INKPEN

作者单位: Department of Computer Science, SITE University of Ottawa Ottawa, ON, Canada

会议类型: 国际会议

会议名称: The 6th International Conference on Natural Language Processing and Knowledge Engineering(第六届IEEE自然语言处理与知识工程国际会议 NLP-KE 2010)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-21（万方平台首次上网日期，不代表论文的发表时间）