Error Diagnosis of Chinese Sentences Using Inductive Learning Algorithm and Decomposition-Based Testing Mechanism

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.