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Multimodal Named Entity Recognition (MNER) is a crucial task for informationextraction from social media platforms such as Twitter. Most current methodsrely on attention weights to extract information from both text and images butare often unreliable and lack interpretability. To address this problem, wepropose incorporating uncertainty estimation into the MNER task, producingtrustworthy predictions. Our proposed algorithm models the distribution of eachmodality as a Normal-inverse Gamma distribution, and fuses them into a unifieddistribution with an evidential fusion mechanism, enabling hierarchicalcharacterization of uncertainties and promotion of prediction accuracy andtrustworthiness. Additionally, we explore the potential of pre-trained largefoundation models in MNER and propose an efficient fusion approach thatleverages their robust feature representations. Experiments on two datasetsdemonstrate that our proposed method outperforms the baselines and achieves newstate-of-the-art performance.

Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion