Compression of Text in Selected Languages-Efficiency, Volume, and Time Comparison

Area of Science:

• Computational Linguistics
• Data Compression
• Natural Language Processing

Background:

• The efficiency of text compression algorithms is crucial for data storage and transmission.
• Natural languages possess inherent complexities that can impact compression ratios.
• Planned languages, like Esperanto, offer a structured linguistic framework that may influence compression performance.

Purpose of the Study:

• To investigate the effectiveness of Esperanto for text compression.
• To compare Esperanto's compression performance against natural languages (Polish and English).
• To evaluate the impact of different compression algorithms on Esperanto text.

Main Methods:

• Text compression was performed using Python with four algorithms: zlib, lzma, bz2, and zl4.
• Four text versions were compressed: Polish, English, Esperanto, and Esperanto in x-notation (ASCII-compatible).
• Analysis focused on compression time and the resulting file sizes before and after compression.

Main Results:

• Esperanto consistently yielded better text compression results than Polish and English across the tested algorithms.
• Compression ratios for Esperanto were significantly more favorable compared to natural languages.
• The study analyzed both compression speed and data reduction effectiveness.

Conclusions:

• The hypothesis that Esperanto offers superior text compression is scientifically confirmed.
• Esperanto's structured nature makes it a more optimal language for data compression tasks.
• This research provides empirical evidence for Esperanto's utility in computational linguistics and data compression.