This repository contains three different models for Arabic Named Entity Recognition (NER). The models are based on BERT, RNN, and TF-IDF techniques, respectively.
Please refer to the BERT Readme for detailed information about the BERT-based NER model.
For information about the RNN-based NER model, please see the RNN Readme.
Details about the TF-IDF-based NER model can be found in the TF-IDF Readme.
Named Entity Recognition (NER) is a subtask of natural language processing that aims to identify and classify named entities in text. Arabic Named Entity Recognition specifically focuses on recognizing named entities in Arabic language texts. It plays a crucial role in various applications, such as information extraction, question answering, sentiment analysis, and machine translation.
Arabic NER is a challenging task due to the unique characteristics of the Arabic language, including rich morphology, complex word structures, and extensive use of inflections. Despite its importance, there is a limited number of models available for Arabic NER compared to other languages. This repository addresses this gap by presenting three different models, each employing a different technique, to perform Arabic NER.
The significance of this work lies in the scarcity of Arabic NER models. By developing and presenting three distinct models based on BERT, RNN, and TF-IDF, this repository offers valuable resources for researchers and practitioners interested in Arabic NER. These models can serve as a foundation for further research, improvements, and applications in the field of Arabic NER.
The three models cater to different preferences and requirements, providing options for researchers to explore and choose the most suitable approach for their specific tasks. They can serve as starting points for developing more advanced and robust Arabic NER systems.
Overall, this work contributes to the advancement of Arabic NER research and expands the possibilities for leveraging named entity recognition in various Arabic language applications. Researchers, developers, and language enthusiasts can benefit from this repository by gaining insights, exploring the models, and building upon the foundation laid by these three distinct approaches.