Abstract
This paper presents Arabic named entity recognition models by employing the single-task and the multi-task learning paradigms. The models have been developed using character-based contextualized Embeddings from Language Model (ELMo) in the input layers of the bidirectional long-short term memory networks. The ELMo embeddings are quite capable of learning the morphology and contextual information of the tokens in word sequences. The single-task learning models outperformed the multi-task learning models and achieved micro F1-scores of 0.8751 and 0.8884 for the flat and nested annotations, respectively.- Anthology ID:
- 2023.arabicnlp-1.88
- Volume:
- Proceedings of ArabicNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore (Hybrid)
- Editors:
- Hassan Sawaf, Samhaa El-Beltagy, Wajdi Zaghouani, Walid Magdy, Ahmed Abdelali, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Khalil Mrini, Rawan Almatham
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 783–788
- Language:
- URL:
- https://aclanthology.org/2023.arabicnlp-1.88
- DOI:
- 10.18653/v1/2023.arabicnlp-1.88
- Cite (ACL):
- Toqeer Ehsan, Amjad Ali, and Ala Al-Fuqaha. 2023. AlphaBrains at WojoodNER shared task: Arabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations. In Proceedings of ArabicNLP 2023, pages 783–788, Singapore (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- AlphaBrains at WojoodNER shared task: Arabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations (Ehsan et al., ArabicNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.arabicnlp-1.88.pdf