以聲符部件為主之漢字學習系統設計研究

The Design of Chinese Character Learning System Based on Phonetic Components

有越來越多人把中文當作第二外語學習，本文目的是使其輕鬆識字。在常用字方面，形聲字約佔60%，為此我們提出以聲符部件為主的漢字識字教學，再匹配適合的發音規則幫助漢字的學習。本系統類似於以字帶字識字教學，達到教導少量部件與少量部首，卻認識大量的字。最後與中文專家合作，從中研院漢字構形資料庫，分析出1453筆常用部件配合形音義及字詞教材，建立以聲符部件為主之漢字識字線上學習系統，並且由發音強度、筆畫數及其延伸字出現頻率作為教學順序。前400個部件與其延伸字時，對於一般文章，可達到60%以上的識字率。前800個部件就可達到9成左右的識字率。

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, 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, we propose an order of phonetic components based on pronunciation strength, 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 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.