# An Easy Partition Approach for Joint Entity and Relation Extraction
## 4 Experiments
### Ablation Studies  
We perform an ablation study on the ADE dataset to check the contribution of each element to the final performance of EPRE in NER and RC tasks. It involves three aspects: gate module, feature interaction strategy, and partition granularity. Table 5 shows the results.  
Figure 4: The F1 score of the entity is extracted under the condition of different entity densities. According to the average frequency of entities, we divide them into three categories: dense \(\geq\) 10%, 10% > dense \(\geq\) 5%, and dense < 5%.  
Figure 3: Partial matching F1 score of relational triples extracted from sentences with different numbers of triples in the WebNLG dataset. We divide sentences into five categories. Each category contains sentences that have 1, 2, 3, 4, and \(\geq\) 5 triples.  
**Gate module.** We compare the results without using the gate module while using the add gate module and the cat gate module. Without the gate module, we directly apply the feature blocks obtained from the PLM for the NER and RC units. In the add gate module, we replace the cat operation with an add operation. Take the NER unit as an example, that \(\mathbf{h_{ge}}=[g\circ\mathbf{h_{net}}]+[(1-g)\circ\mathbf{h_{share}})\). Compared with the case without a gate, the experiment shows that the gate module is effective, and the cat gate module is more effective than the add gate module. This may be because the cat module can maintain the independence of each part of the feature than add a module.  
**Feature interaction strategy.** We compare three feature interaction approaches. In Table 4, the proportional value represents the feature proportion of the NER, share, and RE units. For example, 1:1:1 means that the three-part feature dimension is equal to _d_/3, where \(d\) is the dimension of the PLM. We compare the results under three partition proportions, and the effect is best when the proportion is equal. When we assign a large proportion of the features to a specific task, the proportion of shared features shrinks, and the model performance decreases by 0.8% and 1.7% on the NER and RE tasks, respectively. Compared to the model performance in the three-split case, performance is best when the three-part feature dimensions have equal sizes, which indicates the importance of shared features and dependence features in both tasks.  
**Partition granularity.** We try three partition granularity. One partition means directly using the pretraining features to complete two subtasks, and the features of two specific tasks are the same. Different from the former, two partitions divide the output of the pretraining model into two partitions, one for the NER task and the other for the RC task. The features of two specific tasks are not shared. Our model has three parts. As can be seen, the approach of using independent task-specific features is better than the approach of taking shared features. We expect the features required for each specific task to be different, but the sharing method cannot separate the features for two specific tasks. We observe that the performance is significantly better when considering both independent and shared features for each specific task simultaneously.