# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 4 Experiments
### Ablation Study  
To assess the contribution of each module within the DEHA framework, core components were progressively removed, and performance differences were compared, as shown in Table 3. The ablation settings are defined as follows.  
* _w/o SAGC_ removes the Semantic-Enhanced Global Contrastive Learning (SAGC) module, replacing it with traditional cross-modal contrastive learning ([PERSON] et al., 2020 [8]), which relies solely on random negative samples for global alignment and disables the text semantic similarity-driven prototype expansion strategy;
* _w/o MS-SLC_ removes the Multi-Scale Spatial Local Contrastive (MS-SLC) learning module, substituting it with single-scale (64\(\times\)64 grid) local feature matching, eliminating the spatial pyramid structure and cross-scale contrast constraints;
* _w/o Cross-Modal Fusion_ removes the adaptive cross-modal feature fusion layer, replacing it with simple concatenation followed by a fully connected layer;
* _w/o Visual Constraint CRF_ removes the visual constraint CRF prediction layer, replacing it with a standard CRF that depends solely on text label transition probabilities without incorporating visual alignment strength as a decoding constraint. According to the results in Table 3,  
1. Removing the SAGC module results in a 2.5% decrease in Precision and a 2.2% reduction in Recall on the Twitter-2017 dataset, indicating that traditional contrastive learning approaches, such as MoCo [8], struggle to address the semantic misalignment between images and text in social media contexts. This module enhances cross-modal semantic consistency by expanding prototypes through textual semantic similarity. Furthermore, the removal of this module leads to a 5.3% drop in Recall on the Twitter-2015 dataset, because the elimination of SAGC's mechanism for selecting the hardest negative samples forces the model to rely solely on random negative samples, resulting in blurred discriminative boundaries and an inability to filter out irrelevant entities in images.
2. When MS-SLC is removed, the F1 scores on Twitter-2015 and Twitter-2017 decline by 3.7% and 2.9%, respectively. MS-SLC captures multi-granularity visual contexts through a three-level spatial grid (64 \(\times\) 64/32 \(\times\) 32/16 \(\times\) 16), whereas single-scale local contrast depends solely on text regions, resulting in a 4.5% drop in Recall due to the absence of multi-scale feature support.
3. Removing the cross-modal fusion layer and resorting to simple concatenation also reduces F1 scores. In scenarios with conflicting text and image information, adaptive cross-modal fusion suppresses interfering visual features via a gating mechanism, whereas concatenation allows erroneous visual signals to contaminate text representations, increasing the misjudgment rate. The standard CRF, by ignoring visual evidence consistency, reduces Precision by 1.3% and Recall by 1.1% for image-dominant entities on Twitter-2017.
4. The standard CRF overlooks the consistency of visual evidence, leading to a 1.3% decrease in Precision and a 1.1% decrease in Recall for \"image-dominant entities\" on the Twitter-2017 dataset. The visually constrained CRF suppresses entity probabilities based on low alignment intensity, whereas the standard CRF excessively relies on textual context, erroneously retaining such entities.
5. Simultaneously removing SAGC and MS-SLC results in F1 score decreases of 4.7% and 4.4% on Twitter-2015 and Twitter-2017, respectively, significantly exceeding the sum of individual module ablation declines, underscoring a nonlinear synergistic effect between global semantic calibration and local spatial contrast.