@article {10.3844/jcssp.2022.792.800,
article_type = {journal},
title = {Boosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM Models },
author = {Mahdi Alsultani, Hamid Sadeq and Aliwy, Ahmed H.},
volume = {18},
number = {9},
year = {2022},
month = {Sep},
pages = {792-800},
doi = {10.3844/jcssp.2022.792.800},
url = {https://thescipub.com/abstract/jcssp.2022.792.800},
abstract = {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%.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}