Foundations of statistical natural language processing
Foundations of statistical natural language processing
Inductive logic programming: issues, results and the challenge of learning language in logic
Artificial Intelligence - Special issue on applications of artificial intelligence
Learning Logical Definitions from Relations
Machine Learning
Word identification for Mandarin Chinese sentences
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Mass production of individual feedback
Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education
Unknown word extraction for Chinese documents
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Detecting errors in English article usage by non-native speakers
Natural Language Engineering
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
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
An Approach to Context-Aware Mobile Chinese Language Learning for Foreign Students
ICMB '09 Proceedings of the 2009 Eighth International Conference on Mobile Business
A classifier-based approach to preposition and determiner error correction in L2 English
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
User input and interactions on Microsoft Research ESL Assistant
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Detection of non-native sentences using machine-translated training data
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Discriminative reordering with Chinese grammatical relations features
SSST '09 Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation
Detection of grammatical errors involving prepositions
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Automatically acquiring models of preposition use
SigSem '07 Proceedings of the Fourth ACL-SIGSEM Workshop on Prepositions
Capturing errors in written Chinese words
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A Chinese E-learning Network Platform Based on Web2.0
ICIII '09 Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Phonological and logographic influences on errors in written Chinese words
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Sentence correction incorporating relative position and parse template language models
IEEE Transactions on Audio, Speech, and Language Processing
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This study presents a novel approach to error diagnosis of Chinese sentences for Chinese as second language (CSL) learners. A penalized probabilistic First-Order Inductive Learning (pFOIL) algorithm is presented for error diagnosis of Chinese sentences. The pFOIL algorithm integrates inductive logic programming (ILP), First-Order Inductive Learning (FOIL), and a penalized log-likelihood function for error diagnosis. This algorithm considers the uncertain, imperfect, and conflicting characteristics of Chinese sentences to infer error types and produce human-interpretable rules for further error correction. In a pFOIL algorithm, relation pattern background knowledge and quantized t-score background knowledge are proposed to characterize a sentence and then used for likelihood estimation. The relation pattern background knowledge captures the morphological, syntactic and semantic relations among the words in a sentence. One or two kinds of the extracted relations are then integrated into a pattern to characterize a sentence. The quantized t-score values are used to characterize various relations of a sentence for quantized t-score background knowledge representation. Afterwards, a decomposition-based testing mechanism which decomposes a sentence into background knowledge set needed for each error type is proposed to infer all potential error types and causes of the sentence. With the pFOIL method, not only the error types but also the error causes and positions can be provided for CSL learners. Experimental results reveal that the pFOIL method outperforms the C4.5, maximum entropy, and Naive Bayes classifiers in error classification.