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n-Gram-Based Text Compression.

Vu H Nguyen1, Hien T Nguyen1, Hieu N Duong2

  • 1Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

Computational Intelligence and Neuroscience
|December 15, 2016
PubMed
Summary

This study introduces an efficient Vietnamese text compression method using n-gram dictionaries. The novel approach achieves a 90% compression ratio, outperforming existing techniques.

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Area of Science:

  • Natural Language Processing
  • Data Compression
  • Computational Linguistics

Background:

  • Efficient text compression is crucial for managing large datasets in digital communication.
  • Existing compression methods may not be optimized for the specific linguistic structures of Vietnamese.
  • The development of specialized compression algorithms can significantly improve data handling efficiency.

Purpose of the Study:

  • To propose and evaluate an efficient method for compressing Vietnamese text.
  • To leverage n-gram dictionaries for enhanced compression ratios.
  • To compare the proposed method against state-of-the-art compression techniques.

Main Methods:

  • Text is segmented into n-grams (ranging from unigrams to five-grams).
  • A sliding window approach (bigram to five-grams) is employed for optimal encoding stream generation.
  • N-grams are encoded using byte representations derived from custom-built n-gram dictionaries.

Main Results:

  • A 2.5 GB Vietnamese text corpus was used to construct n-gram dictionaries (totaling 12 GB).
  • The proposed method achieved an average compression ratio of approximately 90%.
  • Experimental results demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

  • The n-gram dictionary-based compression method is highly effective for Vietnamese text.
  • The approach offers significant compression ratios, making it a valuable tool for data storage and transmission.
  • Further research could explore adaptive dictionary generation for even greater efficiency.