# An Easy Partition Approach for Joint Entity and Relation Extraction
## 4 Experiments
### Comparison with Previous Work  
#### 4.2.1 Main Results  
Table 3 reports the comparative evaluation of our EPRE with the existing methods. We report the average F1 scores of three runs on each dataset. Compared with the previous methods of directly using independent (PURE) or shared (CasRel/TPLinker) encoding for two tasks, our EPRE performs better. These results are expected due to the interaction between the entity and relation units. Compared with PFN, EPRE has slightly lower performance on the NER and RC tasks. We consider there are two reasons for this: (1) The model's performance on WebNLG tends to be saturated with only 5019 training data points. We can observe that the model's F1 scores on both tasks are above 90%, which is well above the manual level. We believe it is difficult for the model to improve substantially with these training data. (2) We compared the data predicted by the two models, and we find that our model tends to miss the selection when the entities in the sentences are connected in parallel, or when the same type of entities appear in the sentences, which we believe is also related to the quality of the dataset. After examining the data, we find that the triples labeled in the dataset are incomplete, leading to our model's entity confusion in recognition. The general error is that the model extracts entities that also satisfy relation type, or that are juxtaposed with entities in the labeling. In relation classification, the model is prone to errors when dealing with synonymous or similar relationships (such as country and birthplace) because only one relation type is labeled in the dataset. However, our model may recognize relations that are similar or synonymous with the labeled type. In this case, the model will judge it as an error, leading to a decrease in the accuracy of our model. Our model also performs poorly in the NER task on the SCIERC dataset. We analyze the main types of errors. We observe that EPRE and PFN perform poorly when facing short entities (entities with only one word), and the F1 values are 54.9% and 56.31%, respectively. Compared to other entities, EPRE and PFN reach 67.17% and 68.39% F1 values on NER, respectively. Models have difficultly accurately identifying short entities. In addition, we analyze the impact of whether the entity is included in the triple on NER performance, and we divide the entity into In_triple and Out_triple classes. We observe that EPRE and PFN on Out_triple F1 values (46.5% and 48.6%, respectively) were significantly lower than in In_triple (75.4% and 76.2%, respectively), which may be due to less contextual information about the entity. However, we believe that our model speed advantage can make up for the small gap in NER performance. At the same time, our model performs slightly better in the RE task than PFN.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**WebNLG**} & \multicolumn{2}{c}{**ADE**} & \multicolumn{2}{c}{**SCIERC**} \\ \cline{2-7}  & **NER** & **RE** & **NER** & **RE** & **NER** & **RE** \\ \hline CasRel [36] & 95.5 & 91.8 & - & - & - & - \\ TPLinker [13] & - & 91.9 & - & - & - & - \\ PURE [3] & - & - & - & - & 66.6 & 35.6 \\ SpERT [37] & - & - & 89.3 & 79.2 & & \\ PFN [15] & 98.0\(\bigstar\) & 93.5\(\bigstar\) & 89.7\(\bigstar\) & 80.3\(\bigstar\) & 68.4\(\bigstar\) & 37.5\(\bigstar\) \\ EPRE & 97.5 & 92.9 & 90.0 & 81.9 & 67.6 & 38.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1 scores for WebNLG, ADE, and SCIERC datasets. The SCIERC dataset uses the SCIBERT model, and the rest use the BERT model. The data with \(\bigstar\) on the superscript are the results we reproduce.