Cascaded RBF-CBiLSTM for Arabic Named Entity Recognition

In this paper, an Arabic dataset known as ANERCorp is classified into three name entities: Person, Location, and Organization. This classification process aims to design a model that used to facilitate the searching tasks of the Arabic named entities. The classification process was done by proposing a hybrid model which consists of Radial Basis Function (RBF) cascaded with a sequential Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BiLSTM). The performance of the proposed model was compared with stand-alone machine learning models: Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), RBF, CNN, and BiLSTM. In addition, the performance of the hybrid model was compared with models from the literature. The attained results show that the proposed model outperforms the other stand-alone models and the models from the literature in term of precision, recall, and F1-score.

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