Bangor University at WojoodNER 2024: Advancing Arabic Named Entity Recognition with CAMeLBERT-Mix
Year: 2024
Type: conference-paper
Abstract: This paper describes the approach and results of Bangor University's participation in the WojoodNER 2024 shared task, specifically for Subtask-1: Closed-Track Flat Fine-Grain NER.We present a system utilizing a transformer-based model called bert-base-arabic-camelbert-mix, fine-tuned on the Wojood-Fine corpus.A key enhancement to our approach involves adding a linear layer on top of the bert-basearabic-camelbert-mix to classify each token into one of 51 different entity types and subtypes, as well as the 'O' label for nonentity tokens.This linear layer effectively maps the contextualized embeddings produced by BERT to the desired output labels, addressing the complex challenges of fine-grained Arabic NER.The system achieved competitive results in precision, recall, and F1 scores, thereby contributing significant insights into the application of transformers in Arabic NER tasks.
Author: Norah Alshammari
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4402671150