# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 4 Experiments
### Compared Baselines  
To evaluate the advancements of the dual-enhanced hierarchical alignment (DEHA) framework in addressing cross-modal semantic bias, fine-grained alignment, and image noise suppression, a comprehensive comparison was conducted with both unimodal and multimodal baseline models. The unimodal baselines include a traditional sequence labeling model based on BiLSTM-CRF [2] (2018) and a BERT-CRF pretraining model [3] (2020), which serve to assess the necessity of multimodal information fusion. The multimodal baselines encompass several representative approaches: the Unified Multimodal Transformer (UMT) [5] (2020), which integrates global image features and text embeddings into Transformer layers for deep fusion via self-attention; ITA [11] (2022), an image-text alignment framework based on cross-modal contrastive learning that suppresses irrelevant visual noise by maximizing the similarity between text entities and related image regions; HVPNeT [24] (2022), a hierarchical visual prefix network that categorizes image features into scene, object, and attribute levels to guide text encoding through prefix attention; DebiasCL [13] (2023), which employs debiased contrastive learning to mitigate visual noise interference via dynamic negative sample sampling; MLNet [14] (2023), a multi-level cross-modal fusion architecture that aligns text with image features at object, scene, and attribute levels to address multi-granularity semantic matching; MPMRC-MNER [25] (2023), a machine reading comprehension framework leveraging multimodal prompt learning to guide the model toward image-text associated entities through learnable prompts; ICAK [15] (2024), an instruction-driven knowledge alignment framework that enhances cross-modal consistency using external knowledge graphs; and HamLearning [16] (2024), a hierarchical adaptive modality learning approach that dynamically integrates visual information across various granularities. The experimental results, presented in Table 2, demonstrate that DEHA achieves the best performance on both the Twitter-2015 and Twitter-2017 datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Modality**} & \multirow{3}{*}{**Methods**} & \multicolumn{6}{c}{**Twitter-2015**} \\ \cline{3-10}  & & & \multicolumn{3}{c}{**Single Type (F1)**} & \multicolumn{3}{c}{**Overall**} \\ \cline{3-10}  & & **PER** & **LOC** & **ORG** & **MISC** & **Pre.** & **Rec.** & **F1** \\ \hline \multirow{2}{*}{Text} & BiLSTM-CRF [2] & 76.77 & 72.56 & 41.33 & 26.8 & 68.14 & 61.09 & 64.42 \\  & BERT-CRF [3] & 84.74 & 80.51 & 60.27 & 37.29 & 69.22 & 74.59 & 71.81 \\ \hline \multirow{7}{*}{Text+Image} & UMT [5] & 85.24 & 81.58 & 63.03 & 39.45 & 71.67 & 75.23 & 73.41 \\  & ITA [11] & 85.6 & 82.6 & 64.4 & 44.8 & - & - & 75.6 \\  & HVPNeT [24] & 85.88 & 82.96 & 62.72 & 41.56 & 73.87 & 76.82 & 75.32 \\  & DebiasCL [13] & 85.97 & 81.84 & 64.02 & 43.38 & 74.45 & 76.13 & 75.28 \\  & MLNet [14] & - & - & - & - & 75.73 & 75.73 & 75.73 \\  & MPMRC-MNER [25] & 85.88 & 83.06 & 66.6 & 42.99 & 77.15 & 75.39 & 76.26 \\  & ICAK [15] & 87.01 & 83.85 & 65.87 & 48.28 & 72.36 & 78.75 & 75.42 \\  & HamLearning [16] & 85.28 & 82.84 & 64.46 & 42.52 & 77.25 & 75.75 & 76.49 \\  & **DEHA (Ours)** & **87.22** & **84.21** & **65.47** & **48.65** & **78.29** & **80.03** & **77.42** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different competitive unimodal and multimodal approaches. These results highlight the significant breakthroughs and advantages of our method in several aspects. 1. Compared to ITA [11] and HVPNeT [24], which rely on static alignment strategies and struggle to capture fine-grained semantic differences, DEHA introduces a dynamic multi-scale alignment module that integrates a three-level visual feature pyramid with a gated attention mechanism, enabling adaptive association of text entities with multi-granularity visual regions, including global scenes, regional semantics, and local details.