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Advanced Learning Chinese Characters Method Based on The Advanced Learning Chinese Characters Method Based on the Characteristics of Component and Character Frequency Chung-Ching Wang ([email protected]), Ming-Liang Wei([email protected]) Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan Yu-Lin Chang ([email protected]), Hsueh-Chih Chen([email protected]) Department of Educational Psychology and Counselling, National Taiwan Normal University, Taipei, Taiwan Yi-Ling Chung ([email protected]), Jon-Fan Hu([email protected]) Department of Psychology, National Cheng Kung University, Tainan, Taiwan Abstract components. Consequently, learners are able to readily and Chinese has been recognized as one of most major languages rapidly recognize and write characters by using components. in the world, and it is evident that more and more people are in- For example, ((pin-yin: mu4, meaning: tree) is a compo- terested in understanding or using Chinese. Thus, developing nent, and two ((tree) can compose 林(pin-yin: lin2, mean- an efficient approach for learning Chinese characters is con- ( 森 sidered as an important issue. Certain previous studies have ing: wood) and three (tree) can constitute (pin-yin: sen1, suggested various methods to learning Chinese characters for meaning: forest). Obviously, we are able to readily figure out the purpose of showing students how to read Chinese charac- their meanings due to such processes. Hence, most cogni- ters. In Chinese, the components can offer learners phonolog- ical and morphological meanings similar to the prefixes and tive strategies frequently use components to teach students suffixes in English, and character frequency provides learners of Chinese how to read characters(Shen, 2004). In addition, a character list which can be widely used in daily life. How- many studies have extensively exploited strategies related to ever, very few studies have considered integrating the charac- teristics of component and character frequency. In this study, character frequency because humans are sensitive to the fre- we have developed an effective and systematic approach for quencies of events in their daily life and remember these learning Chinese characters based on both components and things correctly (Ellis, 2002). Therefore, character frequency character frequency. The purpose of the study is to propose a traditional Chinese character learning metric and to present can help learners memorize characters correctly and can also a method for learning only a few components and then the re- contribute to retention. However, only using this strategy sulting reading of more high frequency characters made up of to learn Chinese is not suitable. Most Chinese characters these components. Combining components and character fre- quency advantages, it can present an effective, systematic and characterized by high frequency have intricate construction, rapid mechanism for learning traditional Chinese characters. such as 謝謝(meaning: thank you) and 對不w(meaning: Keywords: Learning Chinese; Character frequency; Compo- sorry). Beside, frequency is also an important factor with nents. respect to education. Previous studies have the impact of high frequency characters on learning Chinese characters for Background beginners(Wang, Hung, Chang, & Chen, 2008), and found the Chinese is a popular and widely used language in the world, learners could learn approximately 700 high frequency char- and it is quite difficult and complicated to read and write Chi- acters with ease. Therefore, the present study presumes that nese characters. In the field of Chinese character recogni- most learners are able to absorb high frequency characters ef- tion research, previous studies have actively involved Chinese fectively. character encoding strategies(Hayes, 1988), and character A recent study used character network construction recognition strategies, such as meaning recognition(Everson, to establish an efficient strategy for learning Chinese 1998), orthographic effect(Lin, 2000), and the reading characters(Yan, Fan, Di, Havlin, & Wu, 2013). This strat- process(Ke, 1998). These studies have contributed many ap- egy exploits the network of Chinese characters in a hierarchi- proaches to improve Chinese character recognition. Also, cal structure and the weight of the network nodes to develop when learning Chinese characters, one can assume that char- a Chinese character metric called distributed node weight acters that correctly match phonetic and orthographic pat- (DNW). However, the character network of the DNW strat- terns are easier to absorb(Ellis & Beaton, 1993). Moreover, egy merely considers associations between characters and numerous studies have offered well developed strategies for character frequency. Actually, a component can be made learning Chinese characters by using radicals or components. up of many distinctive characters, but most of these are not These studies have briefly indicated that the internal compo- high frequency characters. Reviewing the DNW strategy, it nent structure of a Chinese character helps learners to clearly is suggested that characters having high frequency that are remember that character(Taft & Chung, 1999). Since compo- also composed of many components that constitute high fre- nents are the unit of characters and because they can consist quency characters should be learned first. of many distinct characters, even the characteristics of a com- ponent can provide its meaning, phonological and morpho- In this study, we have tightly integrated the component and logical, because characters have the meaning of their internal character frequency characteristics in order to develop an ap- 3067 proach for creating a character order for learning traditional then computing the Character Score(CHS), accordingly ob- Chinese. Different from the above studies, the present ap- taining the list of Chinese characters as sorted by Character proach has effectively exploited the components that com- Score(CHS) from high to low frequency. pose high frequency characters and the characters having high Component Score. We developed a metric (eq.1) to calcu- frequency and high frequency components in order to con- late the Component Score(COS). Here i represents the com- struct a metric for generating an optimal Chinese character ponent, j represents the character, m represents the largest learning order. The proposed approach indicates that if char- number of components, which is decomposed by the most acters are ordered based on the metric, learners will be able complicated character j, and having component i as part of its to learn simple construction and more significant characters components, and the largest number is 9. k ranges from 0 (a first. As a result, the approach is expected to provide an ef- character consists of a unique component and includes com- fective and systematic order for learning Chinese characters. ponent i) to m(a character consists of m different components That is, by simply learning a few components, students of and includes component i), f j represents the token frequency Chinese can read more characters with high frequency. The of character j which is at k level. The symbol n represents that present study has been designed to create a Chinese character all characters at level k consist of n components. An example learning order designed to enable learners to read character of a character ^ (pin-yin: fei1, meaning: not) is given in Ta- expeditiously and systematically. ble 1. At level 1, one character ^, one component and then f j is 214873, n is 1, k is 0, so the level 1 score is 5.33. At Method level 2, with nine characters and ten components, ∑log( f j) is 32.745, n is 10, k is 1, so the level 2 score is 1.64. At level Material. In this study, our approach was constructed based 3, with 2 characters and 5 components, log( f ) is 9.056, n on the data components and character frequency data for the ∑ j is 5, k is 2, so the level 3 score is 0.264. At level 4, with 2 collected Chinese characters. We retrieved character data for characters and 5 components, log( f ) is 5.185, n is 5, k is the format of character pairs and their components to generate ∑ j 3, so the level 4 score is 0.13. The total character score is component data from Beijing Normal University(Yan et al., 7.364. The purpose of the COS is to pick component i which 2013), and character frequency from the combined character can comprise high token frequencies of characters with a few frequency list of both classical and modern Chinese(Jun Da, components, thus lowering n and leading to a higher score. 2005). Moreover, the aim of parameter 2 is to decrease scores that Component Data. Components are parts that comprise are at a higher level, in that characters at higher levels rep- characters and are associated with Chinese character resent very intricate structures. In this manner, a component acquisition(Shen & Ke, 2007). They have not only their own with a higher score means that it can consist of high token definitions, but also their own unique pronunciation and or- frequencies and characters with a simple structure and few thography. For the purpose of this study, 3,910 traditional components. Chinese characters and 310 traditional Chinese components were retrieved. By mapping these characters into their com- m ∑log( f j) ponents, we are capable of obtaining information about the COS(i) = × 2−k (1) ∑ n associations between characters and components. k=0 Character Frequency Data. Character frequency signifi- Character Score. The Character Score(CHS) metric (eq.2) cantly affects the ability to read and identify characters be- contains the token frequency of character j, COS of compo- cause learners are sensitive to the frequencies of characters nents which constitute character j and the number of compo- seen on a daily basis (Ellis, 2002). We gathered the frequen- nents. Here, u represents the number of components which cies of 3,910 characters from the combined character fre- comprise character j, and f represents the token frequency quency list of classical and modern Chinese(Jun Da, 2005).
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