single-source shortest-path algorithm is performed on the graph to detect and correct general spelling errors at the same time. In the graph model, a directed acyclic graph is generated for each sentence, and the. The hybrid model consists of three components: one graph-based model for generic errors and two independently trained models for specific errors. A hybrid model for Chinese spelling check is presented in this article.
Spelling check for Chinese has more challenging difficulties than that for other languages. Further analyses showed that the incorrect characters ranked among the top 10 candidates in 89% of the phonologically similar errors and in 80% of the visually similar errors. Experimental results show that our algorithm captured 97% of incorrect characters for the 4,100 errors, when the average length of the candidate lists was 104. We inspected whether the incorrect character was indeed included in the candidate list and analyzed whether the incorrect character was ranked at the top of the candidate list. To show its effectiveness, we ran the algorithm to select and rank a list of about 100 candidate characters, from more than 5,100 characters, for the incorrectly written character in each of the 4,100 errors. The algorithm also employs empirical rules to determine the degree of similarity between Chinese phonemes. The algorithm extends the principles of decomposing Chinese characters with the Cangjie codes to judge the visual similarity between Chinese characters. We propose a computing algorithm that aims at replication of incorrect Chinese words. Seventy-six percent of these errors were related to the phonological similarity between the correct and the incorrect characters, 46% were due to visual similarity, and 29% involved both factors. In this article, we report findings of our study on 4,100 erroneous Chinese words. Learning from mistakes is important not just in human learning activities it is also a crucial ingredient in techniques for the developments of student models. Information about students’ mistakes opens a window to an understanding of their learning processes, and helps us design effective course work to help students avoid replication of the same errors.
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