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Related Experiment Video

Updated: Apr 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multilingual part-of-speech tagging with weightless neural networks.

Hugo C C Carneiro1, Felipe M G França1, Priscila M V Lima2

  • 1Systems Engineering and Computer Science Program/COPPE, Universidade Federal do Rio de Janeiro (UFRJ) - Caixa Postal 68511, Cidade Universitária, Rio de Janeiro, Rio de Janeiro 21941-972, Brazil.

Neural Networks : the Official Journal of the International Neural Network Society
|March 22, 2015
PubMed
Summary

This study introduces the multilingual Weightless Artificial Neural Network tagger (mWANN-Tagger) for faster, more accurate part-of-speech tagging across many languages. The novel approach significantly improves efficiency and performance in multilingual natural language processing tasks.

Keywords:
Part-of-speech taggingWeightless neural networks

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Training part-of-speech taggers (POS-taggers) is complex and time-consuming, especially for multiple languages.
  • Existing methods like expectation maximization or weight balancing are computationally intensive.
  • Weightless Artificial Neural Networks (WiSARD) offer faster training but face challenges with multilingual parameter tuning.

Purpose of the Study:

  • To propose and evaluate the multilingual Weightless Artificial Neural Network tagger (mWANN-Tagger) for efficient multilingual POS-tagging.
  • To leverage WiSARD's one-pass learning for agile, language-specific parameter optimization.
  • To address the intractability of multilingual POS-tagging with increasing language numbers.

Main Methods:

  • Developed mWANN-Tagger, a WiSARD-based POS-tagger designed for multilingual applications.
  • Utilized WiSARD's RAM-based architecture for single-pass sentence training and incremental learning.
  • Enabled agile searching of language-specific parameter configurations.

Main Results:

  • mWANN-Tagger achieves state-of-the-art or superior accuracy in multilingual POS-tagging.
  • Demonstrated very low standard deviation in accuracy (below 0.25%), indicating high reliability.
  • Showed that most languages can benefit from the mWANN-Tagger architecture.

Conclusions:

  • mWANN-Tagger offers a highly efficient and accurate solution for multilingual part-of-speech tagging.
  • The one-pass learning capability significantly speeds up the training process.
  • The architecture is robust and adaptable, benefiting a wide range of languages.