# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 4 Experiments
### Compared Baselines
In fine-grained entity recognition tasks, such as identifying brand-related _ORG_ entities, the F1 scores on the two datasets improved by 2.75% and 2.76%, respectively, over HVPNeT. The recognition of ORG entities benefits from the cross-modal abstract association capability of the global semantic enhancement module (SAGC). Organization names often involve complex semantic compositions (such as abbreviations, aliases, and implicit associations with visual symbols). SAGC effectively addresses entity coreference ambiguity by modeling the global semantic consistency between textual descriptions and visual concepts through contrastive learning. 2. Although MPMRC-MNER [25] and ICAK [15] excel in few-shot scenarios, markedly enhancing long-tail entity recognition (e.g., niche brands), their dependence on manual expertise and knowledge bases limits their generalizability. In contrast, DEHA dynamically updates cross-modal alignment knowledge through text similarity expansion, improving the F1 score for long-tail entity recognition, such as \"Springfield\" (_LOC_), by 1.11% over MPMRC-MNER, effectively mitigating semantic bias. Geographic entities are often strongly coupled with specific regions in images (such as landmarks and geographic boundaries), and MS-SLC precisely captures the fine-grained co-occurrence patterns between textual descriptions and visual spatial cues through local feature pyramid alignment. 3. Compared to models like DebiasCL [13], MLNet [14], and HamLearning [16], which rely on predefined noise assumptions and fail to address dynamic noise interference, DEHA increases the F1 score on Twitter-2017 by 1.43% to 1.95%. Particularly in cases with complex image backgrounds, such as interference from the Eiffel Tower in Case 1, the MS-SLC module suppresses irrelevant regional feature responses through multi-scale spatial contrast, boosting the recall rate for the _LOC_ category by 3.79%. 4. Compared to the multi-level fusion architecture MLNet [14] and the MRC-based MPMRC-MNER [25], DEHA's visual constraint CRF layer jointly optimizes label transition probabilities and visual consistency, achieving a precision of 78.09% on \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-2017**} \\ \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{3}{*}{Text} & BiLSTM-CRF [2] & 85.12 & 72.68 & 72.5 & 52.56 & 79.42 & 73.43 & 76.31 \\  & BERT-CRF [3] & 90.25 & 83.05 & 81.13 & 62.21 & 83.32 & 83.57 & 83.44 \\ \hline \multirow{3}{*}{Text+Image} & UMT [5] & 91.56 & 84.73 & 82.24 & 70.1 & 85.28 & 85.34 & 85.31 \\  & ITA [11] & 91.4 & 84.8 & 84 & 68.6 & - & - & 85.72 \\  & HVPNeT [24] & 92.05 & 83.35 & 85.31 & 68.5 & 85.84 & 87.93 & 86.87 \\  & DebiasCL [13] & 93.46 & 84.15 & 84.42 & 67.88 & 87.59 & 86.11 & 86.84 \\  & MLNet [14] & - & - & - & - & 87.36 & 87.36 & 87.36 \\  & MPMRC-MNER [25] & 92.8 & 86.8 & 83.4 & 72.79 & 87.1 & 87.16 & 87.13 \\  & ICAK [15] & 93.99 & 87.24 & 86.24 & 75.76 & 85.13 & 89.19 & 87.12 \\  & HamLearning [16] & 91.43 & 86.26 & 86.66 & 69.17 & 86.99 & 87.28 & 87.13 \\  & **DEHA (Ours)** & **94.06** & **87.94** & **88.07** & **76.1** & **88.32** & **89.6** & **88.79** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._Twitter-2015, an improvement of 1.14%, demonstrating that adaptive feature aggregation effectively reduces misjudgments caused by image interference.