Compiling an Arabic-English Transport Dictionary through Corpus Linguistic Methods

This research aims to compile an Arabic-English Transport Dictionary through Corpus Linguistic Methods. This dictionary is intended for general learners. Types of qualitative descriptive research and this research used a discourse analysis. This research used a discourse analysis of secondary data....

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Bibliographic Details
Main Authors: Della Maura Syafa’ah, Nur Hizbullah
Format: Article
Language:English
Published: Fakultas Tarbiyah Institut Agama Islam Sunan Giri Ponorogo 2023-04-01
Series:Scaffolding: Jurnal Pendidikan Islam dan Multikulturalisme
Subjects:
Online Access:https://ejournal.insuriponorogo.ac.id/index.php/scaffolding/article/view/2546
Description
Summary:This research aims to compile an Arabic-English Transport Dictionary through Corpus Linguistic Methods. This dictionary is intended for general learners. Types of qualitative descriptive research and this research used a discourse analysis. This research used a discourse analysis of secondary data. The source of the data involved dictionary data from Arabic websites containing Arabic and English terms in the field of transportation, namely ootlah.com, and tostpost.com. The data collection technique was through discovering synonyms or similar Arabic and English words in the ootlah.com and tostpost.com. Then, collecting the terms related to transportation. After that, combine them into a table and sort by alphabetical letter. When the vocabulary has been combined, then eliminate the similar vocabulary and move it to the document work page according to the alphabet. The results obtained from this study revealed the vocabulary that was previously collected with a total of 600, then eliminated to around 460 Arabic vocabulary in the field of transportation, with eighteen different links from two websites of ootlah.com and tostpost.com produced by the author. In conclusion, the Arabic-English Transport Dictionary has been well arranged through Corpus Linguistics Methods.
ISSN:2656-4548
2656-4491