n-Gram-Based Text Compression

Abstract EN

We propose an efficient method for compressing Vietnamese text using n-gram dictionaries.

It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset.

Given a text, first, the proposed method splits it into n-grams and then encodes them based on n-gram dictionaries.

In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream.

Each n-gram is encoded by two to four bytes accordingly based on its corresponding n-gram dictionary.

We collected 2.5 GB text corpus from some Vietnamese news agencies to build n-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total.

In order to evaluate our method, we collected a testing set of 10 different text files with different sizes.

The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.