Hybrid Feature Selection Approach for Arabic Named Entity Recognition

2018 ◽  
pp. 452-464
Author(s):  
Miran Shahine ◽  
Mohamed Sakre
Keyword(s):  
Feature Selection ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1485
Author(s):  
Brahim Ait Benali ◽  
Soukaina Mihi ◽  
Ismail El Bazi ◽  
Nabil Laachfoubi
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Feature Selection ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Support Vector ◽  
Text Data ◽  
Impact Performance ◽  
Named Entity ◽  
Feature Selection Approach

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems.

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