Deep Learning Approach for Arabic Named Entity Recognition
Inspired by recent work in Deep Learning that have achieved excellent performance on difficult problems such as computer vision and speech recognition, we introduce a simple and fast model for Arabic named entity recognition based on Deep Neural Networks (DNNs) Named Entity Recognition (NER) is the task of classifying or labelling atomic elements in the text into categories such as Person, Location or Organization. The unique characteristics and the complexity of the Arabic language make the extraction of named entities a challenging task. Most state of the art systems use a combination of various Machine Learning algorithms or rely on handcrafted engineering features and the output of other NLP tasks such as part of speech (POS) tagging, text chunking, prefixes and suffixes as well as a large gazetteer. In this paper, we present an Arabic NER system based on DNNs that automatically learns features from data. The experimental results show that our approach outperforms the model based on Conditional Random Fields by 12.36 points in F measure. Moreover, our model outperforms the state of the art by 5.18 points in Precision and gets very close results in F measure. Most importantly, our system can be easily extended to recognize other named entities without any additional rules or handcrafted engineering features.
CICLing