# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 2 Related Work  
### Multimodal Named Entity Recognition  
Multimodal named entity recognition (MNER) seeks to jointly extract entities, such as people's names and locations, from social media text and image content. Early research primarily relied on attention mechanisms, a technique that emulates human visual \"focusing\" capabilities by computing weighted scores between text word vectors and image region features to facilitate cross-modal feature interaction. For instance, [PERSON] et al. [1] proposed a multimodal disambiguation method that achieved initial alignment of image and text features using attention weights. However, it struggled to handle visual noise common in social media. With the advent of Transformer architectures, which leverage self-attention mechanisms to model global dependencies in parallel, [PERSON] et al. [5] developed the Unified Multimodal Transformer (UMT) model, which enhanced fine-grained feature matching in cross-modal interactions through an entity span detection mechanism, thereby significantly improving entity classification accuracy. Building upon this, [PERSON] et al. [7] further proposed Unified Multimodal Graph Fusion (UMGF), which treats textual tokens and image regions as graph nodes and employs a Graph Convolutional Network (GCN) to model fine-grained semantic associations. Nevertheless, existing research methods still exhibit deficiencies in robustness under complex noisy conditions.  
To address the unresolved issues of fine-grained semantic alignment and insufficient noise robustness in multimodal fusion, research on optimizing fine-grained alignment in noisy environments has been burgeoning. [PERSON] et al. [11] were the first to propose a cross-modal contrastive learning framework (ITA) that maximizes the similarity between textual entities and image regions. Concurrently, [PERSON] et al. [12] introduced a universal matching alignment framework (MAF) that employs a dynamic gating mechanism combined with text-region similarity scores, greatly enhancing alignment accuracy, although it did not resolve the issue of visual bias. Consequently, researchers have further explored dynamic noise suppression strategies. For example, [PERSON] et al. [13] proposed a visual object debiasing method, which filters out low-relevance regions based on semantic relevance scoring to reduce noise. [PERSON] et al. [14] proposed a multi-level alignment architecture (MLNet) that aligns at the object level (e.g., \"iPhone\" with phone), scene level (e.g., \"park\" with lawn), and attribute level (e.g., \"red\" with color), thereby significantly enhancing the accuracy of complex entity recognition. Moreover, [PERSON] et al. [15] utilized an instruction template (ICKA) to guide the model to focus on key semantic generation regions, which improved accuracy in few-shot scenarios. [PERSON] et al. [16] further proposed hierarchical alignment learning that jointly optimizes alignment at the word, phrase, and sentence levels, providing a novel perspective for addressing these challenges.  
However, existing methods still face many other challenges. On one hand, global alignment strategies tend to overlook local semantic associations (e.g., the fine-grained matching between entities and image regions); on the other hand, the coupling effect between visual noise interference and cross-modal semantic alignment limits further improvement in model performance.