Arabic Named Entity Recognition on Social Media based on feature selection techniques using SVM-RFE

With the massive expansion of information in social media, a high demand exists for information retrieval techniques. The recognition of the named entity (NE) such as person, location, organization, etc. has emerged as one of the main tasks in natural language processing. Often, utilizing the entire feature set may not only be time-consuming but may also have a negative effect on performance. Due to the high number of features, it is difficult to identify the subset of features relevant to a given task. In this paper, we apply feature selection methods based on the support vector machine recursive feature elimination (SVM-RFE) to find the optimized feature set. Afterward, an optimized feature set combination is used to identify and classify named entities (NEs) based on the Support Vector Machine (SVM). The proposed method is evaluated using Darwish's dataset (a publicly available benchmark for Arabic NER for social media). Experimental results demonstrate the effectiveness of feature selection in enhancing performance and outperform the most advanced systems