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Slovak morphological tokenizer using the Byte-Pair Encoding algorithm.

Dávid Držík1, Frantisek Forgac1

  • 1Department of Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovak Republic.

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|December 9, 2024
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Summary

This study presents the SlovaK Morphological Tokenizer (SKMT), improving natural language processing by preserving word roots. SKMT enhances Slovak language models, boosting performance in tasks like sentiment analysis.

Keywords:
Language modelMorphological tokenizationSlovak languageWord root integrity

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Lexicography

Background:

  • Traditional tokenization methods often fragment word roots, compromising lexical meaning in Slovak.
  • Existing tokenizers like SlovakBERT and pure Byte-Pair Encoding (BPE) show limitations in maintaining root integrity.
  • Morphological analysis is crucial for accurate linguistic representation in Slovak.

Purpose of the Study:

  • Introduce the SlovaK Morphological Tokenizer (SKMT) for improved text tokenization in Slovak.
  • Integrate Slovak language morphology into the Byte-Pair Encoding (BPE) training process.
  • Enhance the performance and quality of natural language processing (NLP) models for the Slovak language.

Main Methods:

  • Developed SKMT by integrating Slovak morphology using BPE, focusing on word root preservation.
  • Extracted word roots from morphological dictionaries and databases for tokenizer training.
  • Conducted comparative evaluations against SlovakBERT and pure BPE tokenizers using corpus preprocessing.

Main Results:

  • SKMT achieved 99.7% root integrity, significantly outperforming SlovakBERT (90.5%) and pure BPE (93.1%).
  • Models fine-tuned with SKMT on a sentiment classification task showed a 3.5% F1-score improvement over conventional BPE.
  • Further validation demonstrated enhanced performance on the Semantic Textual Similarity (STS) task.

Conclusions:

  • SKMT offers a superior approach to text tokenization for the Slovak language.
  • Integrating morphological information significantly enhances NLP model performance and quality.
  • SKMT represents a key advancement for Slovak NLP research and applications.