An intelligent tutoring system for deaf learners of written English
Assets '00 Proceedings of the fourth international ACM conference on Assistive technologies
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automated scoring using a hybrid feature identification technique
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A machine learning approach to the automatic evaluation of machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Automatic error detection in the Japanese learners' English spoken data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A feedback-augmented method for detecting errors in the writing of learners of English
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Correcting ESL errors using phrasal SMT techniques
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Error detection using linguistic features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Finding question-answer pairs from online forums
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Extracting hyponymy relation between Chinese terms
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Segmentation of multi-sentence questions: towards effective question retrieval in cQA services
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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An important application area of detecting erroneous sentences is to provide feedback for writers of English as a Second Language. This problem is difficult since both erroneous and correct sentences are diversified. In this paper, we propose a novel approach to identifying erroneous sentences. We first mine labeled tree patterns and sequential patterns to characterize both erroneous and correct sentences. Then the discovered patterns are utilized in two ways to distinguish correct sentences from erroneous sentences: (1) the patterns are transformed into sentence features for existing classification models, e.g, SVM; (2) the patterns are used to build a rule-based classification model. Experimental results show that both techniques are promising while the second technique outperforms the first approach. Moreover, the classification model in the second proposal is easy to understand, and we can provide intuitive explanation for classification results.