Named entity recognition (NER) is considered as one of the important tasks of natural languages processing (NLP). This repository presents two approaches (machine and deep learning models )that were developed for Arabic named entity recognition (ANER).

This work for the paper referenced below [1].

The first approach is based on a traditional machine learning method of using the conditional random fields (CRF) trained with predefined set of syntactic and morphological features.

Whereas, the second approach is based on the bidirectional long short-term memory with a conditional random fields layer (Bi-LSTM-CRF).

Dataset:

Paper: A Hybrid Approach to Features Representation for Fine-grained Arabic Named Entity Recognition.

Link: https://fsalotaibi.kau.edu.sa/Pages-Arabic-NE-Corpora.aspx

[1]:

Title: Using Bidirectional Long Short-Term Memory and Conditional Random Fields for Labeling Arabic Named Entities: A Comparative Study.

Cite:

@inproceedings{sa2018using, title={Using Bidirectional Long Short-Term Memory and Conditional Random Fields for Labeling Arabic Named Entities: A Comparative Study}, author={Sa'a, D A Alzboun and Tawalbeh, Saia Khaled and Al-Smadi, Mohammad and Jararweh, Yaser}, booktitle={2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS)}, pages={135--140}, year={2018}, organization={IEEE} }