In this paper, we study the transferability of Named Entity Recognition (NER) models between Arabic dialects. This question is important because the available manually-annotated resources are not distributed equally across dialects: Modern Standard Arabic (MSA) is much richer than other dialects for which little to no datasets exist. How well does a NER model, trained on MSA, perform on other dialects? To answer this question, we construct four datasets. The first is an MSA dataset extracted from the ACE 2005 corpus. The others are datasets for Egyptian, Morocan and Syrian which we manually annotate following the ACE guidelines. We train a span-based NER model on top of a pretrained language model (PLM) encoder on the MSA data and study its performance on the other datasets in zero-shot settings. We study the performance of multiple PLM encoders from the literature and show that they achieve acceptable performance with no annotation effort. Our annotations and models are publicly available (https://github.com/niamaelkhbir/Arabic-Cross-Dialectal-NER).

The Wojood Named Entity Recognition (NER) shared task introduces a comprehensive Arabic NER dataset encompassing both flat and nested entity tasks, addressing the challenge of limited Arabic resources. In this paper, we present our team LIPN approach to addressing the two subtasks of WojoodNER SharedTask. We frame NER as a span classification problem. We employ a pretrained language model for token representations and neural network classifiers. We use global decoding for flat NER and a greedy strategy for nested NER. Our model secured the first position in flat NER and the fourth position in nested NER during the competition, with an F-score of 91.96 and 92.45 respectively. Our code is publicly available (https://github.com/niamaelkhbir/LIPN-at-WojoodSharedTask).

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.