# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 4 Experiment
### Result and Analysis
#### 4.4.2 Result Analysis on Different Sentence Types  
Since overlapping triple extraction is a challenging problem for joint extraction, the majority of the models in Table 2 have low F1 scores. Specifically, the statistics in Table 1 show that most of the sentences in the NYT dataset belong to the normal class, while in the WebNLG dataset, most of the sentences belong to the EPO and SEO classes. This fails most previous models to extract overlapping triples. To explore the performance of our model on extracting triples, we extracted triplets from sentences with different triplets and compared them with baseline models, and the results are shown in Table 3. As seen in the table, the majority of the baseline models on both datasets exhibit a diminishing trend in their capacity to extract triples as the number of triples (\(N\)) in the phrase grows, whereas our model shows a rising trend. At \(N=4\), our model surpasses the DualDec model, which has the highest F1 score of the baseline models, while both show an upward trend. Our model outperforms the DualDec model in F1 scores for the NYT dataset, with F1 scores improving by 0.7%, 0.8%, 0.3%, 0.4%, and 0.5%, respectively. Compared with DualDec, our model improves by 3.6%, 0.6%, 4.8%, 2.5%, and 6.3% on the WebNLG dataset, respectively. It shows that our model can deal with complex multi-relationship problems in sentences. Further, to investigate the performance of our model in extracting overlapping relational triples, we extracted triples of different overlapping triples types from the sentences. According to different overlapping categories, the sentences used in the test can be divided into three types, including normal, SEO, and EPO. As shown in Figure 5, our method achieves the best result. For normal class, i.e., not containing overlapping relationships, our model has a 0.7% higher F1 score than the highest baseline model on the NYT dataset and outperforms most of the baseline models on the WebNLG dataset, and its F1 score was only 0.2% lower than CasRel. For single entity overlap (SEO) and entity pair overlap (EPO), the F1 score of our model is higher than the best baseline model on both datasets. Compared to the baseline model with the highest F1 score, our model improves by 0.7% and 0.3% on the SEO class of NYT and WebNLG, respectively. Additionally, our model improves by 0.6% and 0.3% for the EPO class. Compared to the CasRel model, the SGNNet\({}_{WG}\) model improves by 0.2% for the NYT dataset and decreases by 0.2% for the WebNLG dataset in the SEO class, and it improves by 0.1% for NYT dataset and 0.2% for the WebNLG dataset in EPO class. Similarly, for SGNet\({}_{WG}\), compared with SGNet, the improvement of SGNet model is small, but higher than the CasRel model. Since the CasRel model has a certain effect in dealing with overlapping triples, and the effect of the SGNet model is due to this, our model is more capable of dealing with overlapping triples. Moreover, as we described earlier, when the model F1 score reaches 90+, it has saturated and there is very little room for improvement.