Building a Multilingual Vocabulary Database: A Comprehensive Study of 24 Global and Local Languages

Abstract

The ability to communicate in more than one language is becoming increasingly important in a globalised society, and it is closely related to vocabulary learning, which is central to communicative competence —a multifaceted notion defined by Hymes (1967), of which ‘grammatical competence’ includes vocabulary as one of its components. In light of the significance of vocabulary for language teaching and learning, this research aimed to design an extensive vocabulary learning application for 20 international and six local Pakistani languages. At the first step, a corpus of the Urdu language was compiled, and a wordlist was prepared based on that corpus using corpus analysis software. Subsequently, a total of the most common 1,000 content words were selected and organised semantically. The top 18 most widely spoken languages in the world were obtained, and six indigenous languages from Pakistan were also included. Using AI, the vocabulary elements were translated into all these languages, and the translated texts were proofread using Google and Google Images. Translated words with accompanying pictures were also added. This database will facilitate the cross-examination of areas in phonology, morphology, translation, and culture.. Blended learning outlines an international education approach that alternates between mobility and online learning for teachers, students, and tourists in the previously chosen languages.