Jōyō kanji as core building blocks of the Japanese writing system
Some observations from database construction
Bibliographic Data
| ID | 19509330 |
|---|---|
| Authors | Terry Joyce (0000-0001-9625-1979, Tama University), Hisashi Masuda (0000-0002-1857-2096, Hiroshima Shudo University), Taeko Ogawa (0009-0000-8374-0933, Tokai Gakuin University) |
| Year | 2014 |
| Volume | 17 |
| Issue | 2 |
| Pages | 173-194 |
| Publication date | 2014-09-30 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Written Language & Literacy (JOURNAL) |
| Journal identifiers | ISSN: 1387-6732 • E-ISSN: 1570-6001 |
| Publisher | John Benjamins Publishing Company (PUBLISHER • NL) |
| DOI | 10.1075/wll.17.2.01joy |
| OpenAlex | W2013582581 |
| Language | EN |
| Citations received | 4 |
| References cited | 4 |
The architecture of writing systems metaphor has special relevance for understanding the structural nature of the Japanese writing system, and, more specifically, for appreciating how the 2,136 kanji of the 常用漢字表 /jō-yō-kan-ji-hyō/* ‘List of characters for general use’ function as the core building blocks in the orthographic representation of a considerable proportion of the Japanese lexicon. In seeking to illuminate the multiple layers of internal structure within Japanese kanji, the Japanese lexicon, and the Japanese writing system, the paper draws on insights and observations gained from an ongoing project to construct a large-scale Japanese lexical database system. Reflecting structural distinctions within the database, the paper consists of three main sections addressing the different structural levels of kanji components, jōyō kanji, and the lexicon. Keywords: Japanese writing system; building blocks; jōyō kanji; components; orthographic structure; database
Chinese characters · Kanji · Lexicon · Linguistics · Natural language processing · Programming language · Writing system · Computer Science · Natural Language Processing Techniques · Reading and Literacy Development · Second Language Acquisition and Learning · Artificial Intelligence
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,44 |
| Citation span | 2017 - 2025 (9) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |