TY  - JOUR
AU  - Mahdi Alsultani, Hamid Sadeq 
AU  - Aliwy, Ahmed H. 
PY  - 2022
TI  - Boosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM Models 
JF  - Journal of Computer Science
VL  - 18
IS  - 9
DO  - 10.3844/jcssp.2022.792.800
UR  - https://thescipub.com/abstract/jcssp.2022.792.800
AB  - Named-Entity-Recognition(NER) is one of the most important Information-Extraction (IE) use cases, whichis used to improve the performance of Natural Languages Processing (NLP) tasks,such as Relation-Extraction (RE), Question-Answering (QA).  Recently, Arabic NER is tackled in differentways by researchers. In this study, we assess the performance of two widelyused models, namely, LSTM and Bi-LSTM on the NER task in the Arabic languageand perform a comparative study between these models. In contrast to thetraditional data partition technique widely used during the training, we employthe technique of k-fold cross-validation to improve the performance of eachmodel. The experimental results reveal that the performance of all models isimproved when k-fold cross-validation is applied. Additionally, according toour experiment results, the Bi-LSTM model outperforms the LSTM model in termsof our evaluation metric. We achieve the best F1 score of 94.17% withCNN-Bi-LSTM-CRF. An ablation study on k-fold cross-validation demonstrates thatthe F1 score increased from 87.28 to 94.17%.