Character Set Construction for C hinese Language Learning (2021)

Abstract: To promote efficient learning of Chinese characters, pedagogical materials may present not only a single character, but a set of characters that are related in meaning and in written form. This paper investigates automatic construction of these character sets. The proposed model represents a character as averaged word vectors of common words containing the character. It then identifies sets of characters with high semantic similarity through clustering. Human evaluation shows that this representation outperforms direct use of character embeddings, and that the resulting character sets capture distinct semantic ranges.

## introduction to promote efficient vocabulary acquisition, pedagogical materials may present the learner with a set of related words, rather than a single word. the set often consists of words belonging to the same "family"; for english, family members may share the same root, such as the words special, specialize, specialty, especially, etc. these families can be constructed in a straightforward manner from morphological databases and analyzers. 1 an analogous strategy for teaching chinese is the "character family", i.e. a set of characters that are similar in meaning and written form. a natural criterion for family membership is the semantic component, or semantic radical, of the character. the family based on the component 'sun', for example, includes the characters for 'sunny', 'sunshine' and 'dawn' as members ( in comparison to word families in english, character families tend to exhibit less semantic regularity. some family members may have unrelated meaning, or have obscured semantic relation in modern chinese. for example, the character cuò 'wrong' belongs to the 'metal' family, and the character zuó 'past' belongs to the 'sun' family ( this paper investigates automatic construction of character sets. the proposed method can be expected to expedite the generation of these sets for more components and for a larger variety of semantic categories, with the goal of enhancing the coverage and effectiveness of call applications for learning chinese. ## research questions for each semantic component, we define its "character family" to consist of all characters that contain the component. as shown in character representation (q1) the character representation should reflect the "overall" meaning of the character in a variety of contexts. we compare the use of character and word embeddings in constructing character sets (section 4). subfamilies (q2) all family members are traditionally viewed as capturing the general meaning of its semantic component. we investigate whether some families can be clustered into subfamilies to produce character sets with more tightly related meaning (section 5). ## background chinese words are composed of characters. according to @xcite , 81% of the characters are "semantic-phonetic compounds", which can be decomposed into two components. the phonetic component gives pronunciation clues. the semantic component, often used for organizing characters into families ( ## character representation we address q1 by evaluating two character representations for the character set construction task: given a family f , identify a subset of n characters, say @xmath0, ..., c n } ⊆ f , that have the most similar or related meaning. ## conclusion we have presented the first quantitative study on automatic construction of chinese character sets to facilitate language learning. we have evaluated a number of methods for character representation and family clustering. experimental results showed that averaged word vectors achieved statistically significant improvement over direct use of character vectors. further, k-means clustering produced subfamilies that yielded character sets with distinctive meaning. it is hoped that these methods will help expand the variety and coverage of character 7 statistically significant at @xmath1.025 by t-test 8 statistically significant at @xmath2.002 by t-test 9 at @xmath3.27