Arabic Fine-Grained Entity Recognition

• URL: http://arxiv.org/abs/2310.17333v2
• Date: Mon, 18 Dec 2023 18:55:49 GMT
• Title: Arabic Fine-Grained Entity Recognition
• Authors: Haneen Liqreina, Mustafa Jarrar, Mohammed Khalilia, Ahmed Oumar El-Shangiti, Muhammad Abdul-Mageed
• Abstract summary: This article aims to advance Arabic NER with fine-grained entities. Four main entity types in Wojood, geopolitical entity (GPE), location (LOC), organization (ORG), and facility (FAC) are extended with 31 subtypes. To do this, we first revised Wojood's annotations of GPE, LOC, ORG, and FAC to be compatible with the LDC's ACE guidelines. All mentions of GPE, LOC, ORG, and FAC in Wojood are manually annotated with the LDC's ACE sub-types.
• Score: 14.230912397408765
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional NER systems are typically trained to recognize coarse-grained entities, and less attention is given to classifying entities into a hierarchy of fine-grained lower-level subtypes. This article aims to advance Arabic NER with fine-grained entities. We chose to extend Wojood (an open-source Nested Arabic Named Entity Corpus) with subtypes. In particular, four main entity types in Wojood, geopolitical entity (GPE), location (LOC), organization (ORG), and facility (FAC), are extended with 31 subtypes. To do this, we first revised Wojood's annotations of GPE, LOC, ORG, and FAC to be compatible with the LDC's ACE guidelines, which yielded 5, 614 changes. Second, all mentions of GPE, LOC, ORG, and FAC (~44K) in Wojood are manually annotated with the LDC's ACE sub-types. We refer to this extended version of Wojood as WojoodF ine. To evaluate our annotations, we measured the inter-annotator agreement (IAA) using both Cohen's Kappa and F1 score, resulting in 0.9861 and 0.9889, respectively. To compute the baselines of WojoodF ine, we fine-tune three pre-trained Arabic BERT encoders in three settings: flat NER, nested NER and nested NER with subtypes and achieved F1 score of 0.920, 0.866, and 0.885, respectively. Our corpus and models are open-source and available at https://sina.birzeit.edu/wojood/.

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