# A Dual-Enhanced Hierarchical Alignment Framework for Multimodal Named Entity Recognition
## 4 Experiments  
### Datasets and Evaluation Metrics  
We conducted experiments using two publicly available multimodal named entity recognition datasets: the Twitter-2015 dataset [4] and the Twitter-2017 dataset [23]. These datasets have been widely adopted in the fields of natural language processing and social media analysis. They are annotated with four entity types: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC). The Twitter-2015 dataset contains 8257 tweets, while the Twitter-2017 dataset includes 4819 tweets, both enriched with extensive multimodal information. Table 1 provides a detailed breakdown of the number of entity types and the total number of tweets across the training, validation, and test sets for both datasets, laying the foundational data support for subsequent experimental analysis.  
To comprehensively evaluate the performance of multimodal named entity recognition models, this paper adopts Precision, Recall, and F1 score as the core evaluation metrics [1], with their computation accounting for fine-grained performance variations across different entity types, such as person names, locations, and organization names. Specifically, the F1 score is reported for each entity category, while the overall model performance is assessed using aggregated metrics. Given the imbalanced distribution of entity categories in the data, reporting individual F1 scores provides clearer insight into the model's performance on each category, whereas the aggregated metrics offer a global perspective.  
Precision measures the proportion of correctly identified positive samples among those predicted as positive, reflecting the accuracy of the predictions:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{15}\]  
Recall quantifies the model's ability to identify all true positive samples, indicating the completeness of detection:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{16}\]  
The F1 score, as the harmonic mean of Precision and Recall, provides a balanced measure of the model's accuracy and robustness:  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Entity Type**} & \multicolumn{3}{c}{**Twitter-2015**} & \multicolumn{3}{c}{**Twitter-2017**} \\ \cline{2-7}  & **Train** & **Dev** & **Test** & **Train** & **Dev** & **Test** \\ \hline Person & 2217 & 552 & 1816 & 2943 & 626 & 621 \\ Location & 2091 & 522 & 1697 & 731 & 173 & 178 \\ Organization & 928 & 247 & 839 & 1674 & 375 & 395 \\ Miscellaneous & 940 & 225 & 726 & 701 & 150 & 157 \\ Total & 6176 & 1546 & 5078 & 6049 & 1324 & 1351 \\ No. of Tweets & 4000 & 1000 & 3257 & 3373 & 723 & 723 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics summary of two Twitter datasets.  
Here, TP (True Positive) denotes the number of correctly identified entities, FP (False Positive) represents the number of non-entities incorrectly identified, and FN (False Negative) indicates the number of true entities missed [1].  
This evaluation strategy effectively captures the model's balanced recognition capabilities across various entity types, making it particularly suitable for social media scenarios characterized by uneven entity type distributions and ambiguous semantic boundaries.