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A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity

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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)

Background:

  • Named Entity Recognition (NER) is crucial for domain-specific NLP applications, but existing Bengali NER models lack accuracy due to linguistic complexity and resource scarcity.
  • High-resource languages like English and Chinese have well-performing NER architectures, highlighting a gap for morphologically rich languages such as Bengali.

Purpose of the Study:

  • To achieve state-of-the-art performance in Bengali Named Entity Recognition (NER) by combining Data-Centric and Model-Centric AI.
  • To propose a method for developing high-quality NER datasets applicable to any language.
  • To evaluate the impact of a high-quality dataset on NER model accuracy.

Main Methods:

  • Developed a unique, high-quality dataset specifically for Bengali NER.
  • Integrated Data-Centric AI (dataset quality) and Model-Centric AI (hybrid deep learning model) approaches.
  • Evaluated various Deep Learning models using the created dataset.

Main Results:

  • A hybrid model achieved an exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%.
  • The study demonstrated the significant impact of a high-quality dataset on NER model performance.
  • The proposed model minimizes the need for manual feature engineering and requires fewer computational resources.

Conclusions:

  • The integrated AI approach and high-quality dataset significantly advance Bengali NER capabilities.
  • The developed method for dataset creation is generalizable to other languages.
  • This work offers a resource-efficient and accurate solution for NER in morphologically rich languages.