# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 5 Conclusions and Outlook  
This paper tackles the challenges of multimodal named entity recognition (MNER) in social media, specifically addressing cross-modal semantic bias--leading to a lack of fine-grained alignment--and image noise. To tackle these problems, we propose a dual-enhanced hierarchical alignment (DEHA) framework. This framework leverages a global-local collaborative optimization strategy by integrating a dual-enhancement mechanism that combines semantic-enhanced global contrastive learning with multi-scale spatial local contrastive learning. These mechanisms strengthen the semantic consistency between image and text modalities at both the sample and feature levels. Furthermore, the framework incorporates a cross-modal fusion layer and a visual-constrained conditional random field (CRF) prediction layer to achieve multi-level fine-grained alignment and automated noise suppression through collaborative decision making. Experimental results demonstrate that DEHA significantly outperforms existing baseline models on mainstream social media datasets, exhibiting superior robustness particularly in complex noise scenarios and fine-grained entity localization tasks. The core innovation of DEHA lies in its seamless integration of global semantic expansion alignment with local spatial alignment, synchronously optimizing inter-modal semantic distributions and spatial associations through a hierarchical alignment strategy. Additionally, the introduction of a visual-guided CRF constraint mechanism ensures that entity recognition outcomes adhere to both textual contextual patterns and visual spatial evidence, thereby providing more reliable multimodal semantic understanding capabilities for downstream applications such as knowledge graph construction and intelligent question answering. Despite the notable advancements achieved by DEHA in cross-modal fine-grained alignment and noise suppression, certain limitations persist. Firstly, the fine-grained semantic alignment still depends on predefined multi-scale divisions, which may overlook implicit cross-scale associations. Incorporating an adaptive scale selection mechanism or leveraging graph neural networks to model hierarchical semantic relationships could further enhance alignment precision. Secondly, the deep integration of contrastive learning with large language models (LLMs) represents an important future direction. Leveraging the semantic generation capabilities of LLMs could facilitate the construction of high-quality cross-modal supervision signals, thereby enhancing the robustness of global alignment. In terms of cross-domain adaptability, the hierarchical alignment mechanism of DEHA demonstrates potential for transferability to other multimodal scenarios. For instance, in medical text analysis, global semantic enhancement could capture the complex relationships between medical images and diagnostic reports, while local spatial calibration \begin{table}
\begin{tabular}{c} Louvre Museum (ORG) \(\checkmark\) \\ Paris (LOC) \(\checkmark\) \\ \end{tabular} & Apple (ORG) \(\checkmark\) \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._could optimize fine-grained localization of anatomical structures. In e-commerce scenarios, the framework could support multimodal understanding of product images and textual descriptions, capturing deep associations between category features and attribute details through dual semantic-spatial enhancement, thereby offering a general paradigm for cross-domain multimodal modeling. To address the challenge of computational complexity in practical deployments, future work will explore lightweight engineering optimization strategies. By employing multimodal feature caching mechanisms and model compression techniques, the computational load on the visual encoder can be effectively reduced, while dynamic feature pre-computation can help balance inference efficiency.