The emergence of multimodal content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasingly important area of research within Natural Language Processing. Despite progress in high-resource languages such as English, MNER remains underexplored for low-resource languages like Urdu. The primary challenges include the scarcity of annotated multimodal datasets and the lack of standardized baselines. To address these challenges, we introduce the U-MNER framework and release the Twitter2015-Urdu dataset, a pioneering resource for Urdu MNER. Adapted from the widely used Twitter2015 dataset, it is annotated with Urdu-specific grammar rules. We establish benchmark baselines by evaluating both text-based and multimodal models on this dataset, providing comparative analyses to support future research on Urdu MNER. The U-MNER framework integrates textual and visual context using Urdu-BERT for text embeddings and ResNet for visual feature extraction, with a Cross-Modal Fusion Module to align and fuse information. Our model achieves state-of-the-art performance on the Twitter2015-Urdu dataset, laying the groundwork for further MNER research in low-resource languages.

该研究解决了乌尔都语缺乏多模态命名实体识别（MNER）数据集和基准的挑战，提出了U-MNER框架，并发布了Twitter2015-乌尔都语数据集。通过使用Urdu-BERT和ResNet集成文本和视觉信息，该模型在Twitter2015-乌尔都语数据集上取得了最先进的性能，为低资源语言的MNER研究奠定了基础。

乌尔都语多模态命名实体识别的基准数据集和框架