MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media

Shizhou Huang, Bo Xu, Changqun Li, Jiabo Ye, Xin Lin

Abstract: Recently, multimodal named entity recognition (MNER) has emerged as a vital research area within named entity recognition. However, current MNER datasets and methods are predominantly based on text and a single accompanying image, leaving a significant research gap in MNER scenarios involving multiple images. To address the critical research gap and enhance the scope of MNER for real-world applications, we propose a novel human-annotated MNER dataset with multiple images called MNER-MI. Additionally, we construct a dataset named MNER-MI-Plus, derived from MNER-MI, to ensure its generality and applicability. Based on these datasets, we establish a comprehensive set of strong and representative baselines and we further propose a simple temporal prompt model with multiple images to address the new challenges in multi-image scenarios. We have conducted extensive experiments to demonstrate that considering multiple images provides a significant improvement over a single image and can offer substantial benefits for MNER. Furthermore, our proposed method achieves state-of-the-art results on both MNER-MI and MNER-MI-Plus, demonstrating its effectiveness. The datasets and source code can be found at https://github.com/JinFish/MNER-MI.