Enhancing Georgian Text Processing: Transliteration Techniques
Archil Elizbarashvili, Magda Tsintsadze, Marana Khachidze · Baltic Journal of Modern Computing · 2024
This article explores character encoding within NLP, emphasizing the relevance of UTF-8, especially for Georgian.It examines transliteration as a solution to enhance data processing efficiency, addressing challenges with Georgian characters in NLP tasks.Using Python scripts and shell commands, a comprehensive experiment compares the performance of transliteration and detransliteration.The sed and vim commands demonstrate superior efficiency, especially in handling larger files.The results highlight the consistent advantage of transliterated texts over originals with Georgian characters, with shell commands processing them approximately 18 times faster.Emphasizing the importance of method selection based on task nature and data volume, the article underscores the practical advantages of shell commands, the importance of disk buffering and cache for optimizing data reading and writing processes, especially when dealing with cached data.Overall, the study contributes valuable insights into character encoding complexities, offering practical considerations for optimizing NLP data processing, particularly in languages like Georgian.