@inproceedings{b7406f1c44bf4974b637af21b27c8035,
title = "AlphaBrains at WojoodNER shared task: Arabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations",
abstract = "This paper presents Arabic named entity recognition models by employing single-task and multi-task learning paradigms. The models were developed by using character-based contextualized Embeddings from Language Model (ELMo) in the input layers of the Bidirectional Long-Short Term Memory (BiLSTM) networks. The ELMo embeddings are quite capable of learning the morphology and contextual information of tokens in word sequences. The single-task learning model outperformed the multi-task learning model, achieving micro F1-scores of 0.8751 and 0.8884, respectively, ranking 10th and 7th in the shared task for flat and nested NER.",
author = "Toqeer Ehsan and Amjad Ali and Ala Al-Fuqaha",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 1st Arabic Natural Language Processing Conference, ArabicNLP 2023 ; Conference date: 07-12-2023",
year = "2023",
language = "English",
series = "ArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "783--788",
editor = "Hassan Sawaf and Samhaa El-Beltagy and Wajdi Zaghouani and Walid Magdy and Nadi Tomeh and \{Abu Farha\}, Ibrahim and Nizar Habash and Salam Khalifa and Amr Keleg and Hatem Haddad and Imed Zitouni and Ahmed Abdelali and Khalil Mrini and Rawan Almatham",
booktitle = "ArabicNLP 2023 - 1st Arabic Natural Language Processing Conference, Porceedings",
address = "United States",
}