five

METHODS OF MASS MEDIA TERMINOLOGY CREATION FROM ENGLISH TO ARABIC

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://zenodo.org/records/10183917
下载链接
链接失效反馈
官方服务:
资源简介:
This study discusses creation methods used by terminologists to translate mass media terminology from English to Arabic. In this era of globalisation, various forms of mass media such as the print media, radio, television, film, broadcasting and the internet have already become integrated with society all over the world, including in the Arab world. Thus, the appropriate translation of media terminologies which originate from foreign languages, especially English, is important to ensure that Arabic media remains relevant in the mainstream, in addition to guaranteeing the sustainability and dynamism of the Arabic language itself as the main language of communication in the modern world. However, the linguistic discrepancies between English and Arabic pose a unique challenge to terminologists to produce accurate translations of the terminologies. A total of 40 media terms were selected from entry B of the Mustalahat Ilamiyyah dictionaryas data for this study. Then, the Malay equivalent of each terminology is identified by referring to the official website of Pusat Rujukan Persuratan Melayu developed by Dewan Bahasa dan Pustaka. Next, the list of Arabic terminologies is classified according to the method of Arabic terminology formation and analysed descriptively based on the views of al-Qasimi (2019). This study found that ishtiqaq (derivation) is the most frequently used terminology creation method, followed by tarkib (phrase), prefix and suffix, and tarib (arabization). This study will benefit students and researchers in the field of Arabic linguistics as well as Arab media companies by providing clarification on the methods commonly used by Arab terminologists in constructing technical terms in Arabic, especially in the field of mass media.
创建时间:
2024-07-10
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作