Yi-Hsuan Chuang

Visually and Phonologically Similar Characters in Incorrect Chinese Words: Analyses, Identification, and Applications

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. 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. In this article, we report findings of our study on 4,100 erroneous 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. We propose a computing algorithm that aims at replication of incorrect Chinese words. The algorithm extends the principles of decomposing Chinese characters with the Cangjie codes to judge the visual similarity between Chinese characters. The algorithm also employs empirical rules to determine the degree of similarity between Chinese phonemes. 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. 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. 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. 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.

Two Applications of Lexical Information to Computer-Assisted Item Authoring for Elementary Chinese

Testing is a popular way to assess ones competence in a language. The assessment can be conducted by the students for self evaluation or by the teachers in achievement tests. We present two applications of lexical information for assisting the task of test item authoring in this paper. Applying information implicitly contained in a machine readable lexicon, our system offers semantically and lexically similar words to help teachers prepare test items for cloze tests. Employing information about structures and pronunciations of Chinese characters, our system provides characters that are similar in either formation or pronunciation for the task of word correction. Experimental results indicate that our system furnishes quality recommendations for the preparation of test items, in addition to expediting the process.

Effects of Combining Bilingual and Collocational Information on Translation of English and Chinese Verb-Noun Pairs

We studied a special case of the translation of English verbs in verb-object pairs. Researchers have studied the effects of the linguistic information of the verbs being translated, and many have reported how considering the objects of the verbs will facilitate the quality of translation. In this study, we took an extreme approach-assuming the availability of the Chinese translation of the English object. In a related exploration, we examined how the availability of the Chinese translation of the English verb influences the translation quality of the English nouns in verb phrases with analogous procedures. We explored the issue with 35 thousand VN pairs that we extracted from the training data obtained from the 2011 NTCIR PatentMT workshop and with 4.8 thousand VN pairs that we extracted from a bilingual version of Scientific American magazine. The results indicated that, when the English verbs and objects were known, the additional information about the Chinese translations of the English verbs (or nouns) could improve the translation quality of the English nouns (or verbs) but not significantly. Further experiments were conducted to compare the quality of translation achieved by our programs and by human subjects. Given the same set of information for translation decisions, human subjects did not outperform our programs, reconfirming that good translations depend heavily on contextual information of wider ranges.

An Exploration of Native Speakers' Eye Fixations in Reading Chinese Text

We collected the locations of eye fixations of Chinese native speakers when they read four Chinese articles, and attempted to analyze how the contextual linguistic and personal information influence the landing positions within the landing sites. In addition, we employed machine learning techniques to build models for the prediction of the landing positions. The models performed well for the closed tests, achieving 78% in accuracy in predicting whether a reader’s eyes landed on the first or the second character within a word that contained two characters. Unfortunately, the accuracy for the same task in the 10-fold cross validations dropped to 60%, indicating the necessity of more future work.