Arabic Named Entity Recognition in Arabic Tweets Using BERT-based Models
Brahim Ait Ben Ali, Soukaina Mihi, Nabil Laachfoubi, Addi Ait Mlouk · Procedia Computer Science · 2022
With the large amount of unstructured data being broadcasted every day, building powerful methods enabling information retrieval and extraction becomes necessary. Unfortunately, named entity recognition is a difficult classification task to classify data into predefined labels, which is further challenged by the Arabic language's particular characteristics and complex nature. This work trains six BERT-based models (Bidirectional Encoder Representations from Transformers) and uses a BiLSTM-CRF architecture for the NER task on dialectal Arabic. Our fine-tuning approach yields new state-of-the-art results on publicly available dialectal Arabic social media datasets.