El-Kawaref at WojoodNER shared task: StagedNER for Arabic Named Entity Recognition

Nehal Elkaref, Mohab Elkaref

Abstract: Named Entity Recognition (NER) is the task of identifying word-units that correspond to mentions as location, organization, person, or currency. In this shared task we tackle flat-entity classification for Arabic, where for each word-unit a single entity should be identified. To resolve the classification problem we propose StagedNER a novel technique to fine-tuning NER downstream tasks that divides the learning process of a transformer-model into two phases, where a model is tasked to learn sequence tags and then entity tags rather than learn both together simultaneously for an input sequence. We create an ensemble of two base models using this method that yield a score of on the development set and an F1 performance of 90.03% on the validation set and 91.95% on the test set.

External IDs:dblp:conf/wanlp/ElkarefE23