Automatic Configuration of Deep Learning Algorithms for an Arabic Named Entity Recognition System

Word embedding models have been widely used by many researchers to extract linguistic features for Natural Language Processing (NLP) tasks. However, the creation of an adequate Word embedding model depends on choosing the right language model method and architecture, in addition to finetuning the various parameters of the language model. Each parameter combination could result in a different model, and each model can behave differently according to the targeted NLP task. In this paper, we present an approach that combines a range of Word embedding models, multiple clustering and classification methods, and Irace for automatic algorithm configuration. The goal is to facilitate the construction of the most accurate Arabic Named Entity Recognition (NER) model for our dataset. Our approach involves the creation of different Word embedding models, the implementation of these models in different classification and clustering methods, and finetuning these implementations with different parameter combinations to create an Arabic NER System with the highest accuracy rate.

@article{Chaimae2023,
  title     = {Automatic Configuration of Deep Learning Algorithms for an Arabic Named Entity Recognition System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {AZROUMAHLI Chaimae and MOUHIB Ibtihal and El YOUNOUSSI Yacine and BADIR Hassan},
  doi       = {10.14569/IJACSA.2023.0141012},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141012}
}