http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Japanese Expressions that Include English Expressions
( Masaki Murata ),( Toshiyuki Kanamaru ),( Koichirou Nakamoto ),( Katsunori Kotani ),( Hitoshi Isahara ) 한국언어정보학회 2007 학술대회 논문집 Vol.2007 No.-
We extracted English expressions that appear in Japanese sentences in newspaper articles and on the Internet. The results obtained from the newspaper articles showed that the preposition "in" has been regularly used for more than ten years, and it is still regularly used now. The results obtained from the Internet articles showed there were many kinds of English expressions from various parts of speech. We extracted some interesting expressions that included English prepositions and verb phrases. These were interesting because they had different word orders to the normal order in Japanese expressions. Comparing the extracted English and katakana expressions, we found that the expressions that are commonly used in Japanese are often written in the katakana syllabary and that the expressions that are not so often used in Japanese, such as prepositions, are hardly ever written in the katakana syllabary.
An Attempt to Measure the Familiarity of Specialized Japanese in the Nursing Care Field
Haihong Huang,Hiroyuki Muto,Toshiyuki Kanamaru Institute for Corpus Research 2023 Asia pacific journal of corpus research Vol.4 No.2
Having a firm grasp of technical terms is essential for learners of Japanese for Specific Purposes (JSP). This research aims to analyze Japanese nursing care vocabulary based on objective corpus-based frequency and subjectively rated word familiarity. For this purpose, we constructed a text corpus centered on the National Examination for Certified Care Workers to extract nursing care keywords. The Log-Likelihood Ratio (LLR) was used as the statistical criterion for keyword identification, giving a list of 300 keywords as target words for a further word recognition survey. The survey involved 115 participants of whom 51 were certified care workers (CW group) and 64 were individuals from the general public (GP group). These participants rated the familiarity of the target keywords through crowdsourcing. Given the limited sample size, Bayesian linear mixed models were utilized to determine word familiarity rates. Our study conducted a comparative analysis of word familiarity between the CW group and the GP group, revealing key terms that are crucial for professionals but potentially unfamiliar to the general public. By focusing on these terms, instructors can bridge the knowledge gap more efficiently.