Phonetic Component Ranking and Pronunciation Rules Discovery for Picto-Phonetic Chinese Characters

In recent years, there are a considerable number of new immigrants in Taiwan. Although these people are in the good position to learn Chinese, the advantages are limited to speaking and listening. Recognizing Chinese characters is a tough task since one has to memorize the shape, meaning and pronunciation at the same time. Therefore, the cost of learning a single character is relatively high compared with other languages in alphabet system. The goal of this study is to make the 80% pictophonetic characters to be organized more systematically such that the pronunciation of most pictophonetic characters can be inferred automatically. We evaluate the importance of Chinese components by considering the pronunciation strength, occurring frequency, and number of strokes using linear sum, product, and harmonic mean, respectively. Furthermore, we discover pronunciation rules by association mining with priority grouping. Three groups of high reliability rules and five groups of high support rules are demonstrated in this paper to show the effectiveness of pronunciation rule discovery.