以聲符部件為主之漢字學習系統設計研究

摘要

An increasing number of people learn Chinese as second language in the world. About 60% of Chinese characters are picto-phonetic compounds which are composed of a phonetic component (PC) and semantic component. Therefore炻one can make a guess at a character’s pronunciation and meaning from its phonetic and semantic component for a new character. For this reason炻we propose an order of phonetic components based on pronunciation strength炻frequency and number of strokes for efficient learning with proper pronunciation rules and graph recognition. We adopt stem-deriving instructional method which extends each phonetic component with different radical component to derive new picto-phonetic compounds of similar pronunciation. Via simulation, the top 400 phonetic components and their Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (ROCLING 2012) 112 picto-phonetic extensions are enough for the recognition of 60% characters in general articles; and top 800 phonetic components can help recognition of 90% characters of general news articles