Detecting word ordering errors in chinese sentences for learning chinese as a foreign language
Journal
24th International Conference on Computational Linguistics
Pages
3003-3018
Date Issued
2012
Author(s)
Yu C.-H.
Abstract
Automatic detection of sentence errors is an important NLP task and is valuable to assist foreign language learners. In this paper, we investigate the problem of word ordering errors in Chinese sentences and propose classifiers to detect this type of errors. Word n-gram features in Google Chinese Web 5-gram corpus and ClueWeb09 corpus, and POS features in the Chinese POStagged ClueWeb09 corpus are adopted in the classifiers. The experimental results show that integrating syntactic features, web corpus features and perturbation features are useful for word ordering error detection, and the proposed classifier achieves 71.64% accuracy in the experimental datasets. ? 2012 The COLING.
Subjects
Clueweb09
Computer-aided language learning
HSK corpus
Word ordering error detection
Type
conference paper