Integrating Semantic Features for Enhancing Arabic Named Entity Recognition

Named Entity Recognition (NER) is currently an essential research area that supports many tasks in NLP. Its goal is to find a solution to boost accurately the named entities identification. This paper presents an integrated semantic-based Machine learning (ML) model for Arabic Named Entity Recognition (ANER) problem. The basic idea of that model is to combine several linguistic features and to utilize syntactic dependencies to infer semantic relations between named entities. The proposed model focused on recognizing three types of named entities: person, organization and location. Accordingly, it combines internal features that represented linguistic features as well as external features that represent the semantic of relations between the three named entities to enhance the accuracy of recognizing them using external knowledge source such as Arabic WordNet ontology (ANW). We introduced both features to CRF classifier, which are effective for ANER. Experimental results show that this approach can achieve an overall F-measure around 87.86% and 84.72% for ANERCorp and ALTEC datasets respectively.

@article{Alsayadi2016,
  title     = {Integrating Semantic Features for Enhancing Arabic Named Entity Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {3},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Hamzah A. Alsayadi and Abeer M. ElKorany},
  doi       = {10.14569/IJACSA.2016.070318},
  url       = {https://doi.org/10.14569/IJACSA.2016.070318}
}