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Thai Word Segmentation with a Brain-Inspired Sparse Distributed Representations Learning Memory.

Thasayu Soisoonthorn1, Herwig Unger2, Maleerat Maliyaem1

  • 1King Mongkut's University of Technology North Bangkok, Faculty of Information Technology and Digital Innovation, Bangkok, Thailand.

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Two novel brain-inspired methods enhance Thai word segmentation using Sparse Distributed Representations (SDRs). These approaches significantly improve accuracy and performance over existing methods, with one achieving near-perfect scores on learned vocabularies.

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Area of Science:

  • Natural Language Processing
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Thai language presents unique challenges for word segmentation due to its unsegmented nature.
  • Incorrect word segmentation severely impacts the performance of downstream Natural Language Processing (NLP) tasks.
  • Existing segmentation methods often struggle with accuracy and context-dependent word identification.

Purpose of the Study:

  • To introduce two novel brain-inspired computational methods for accurate Thai word segmentation.
  • To leverage Sparse Distributed Representations (SDRs), inspired by neocortex structure, for improved information processing in NLP.
  • To evaluate the proposed methods against established and state-of-the-art Thai word segmentation techniques.

Main Methods:

  • Development of THDICTSDR: A dictionary-based method enhanced with SDRs for contextual learning and n-gram integration.
  • Development of THSDR: A dictionary-free method utilizing SDRs for word segmentation.
  • Comparative evaluation using BEST2010 and LST20 datasets against Longest Matching, NewMM, and Deepcut (state-of-the-art deep learning).

Main Results:

  • THDICTSDR demonstrated significantly improved accuracy and performance over traditional dictionary-based methods.
  • THDICTSDR achieved an F1-Score of 95.60%, comparable to Deepcut's 96.34%, and excelled with 96.78% on learned vocabularies.
  • THSDR showed fault tolerance to noise and outperformed deep learning methods in all tested cases, achieving 99.48% F1-Score on fully learned sentences.

Conclusions:

  • The proposed brain-inspired methods, particularly THDICTSDR, offer a substantial advancement in Thai word segmentation accuracy and efficiency.
  • SDRs provide a powerful mechanism for contextual understanding and robust performance in NLP tasks, mimicking neural processing.
  • Both THDICTSDR and THSDR present viable and effective alternatives to current state-of-the-art segmentation approaches, with THSDR offering unique noise-handling capabilities.