# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
## 5 Results and Discussion
### Discussion of Results on Other Datasets  
When we tested our model on the GENIA dataset, we applied the PNER II framework and initially compared it with the model introduced by [PERSON] et al. [9]. In their research, they presented two named entity recognition methods. The first method employs an LSTM-CRF neural model framework, which was designed to tackle nested entities through the integration of label layers. Our model builds upon this approach, capitalizing on its simplicity and efficiency for improvement. The second method they proposed uses a sequence-to-sequence (seq2 seq) approach, and both methods have shown commendable performance. We compared our model against both of these methods, as detailed in Table 14.  
The comparison results revealed that, by using the same LSTM-CRF framework, our model achieved significant performance improvements. These improvements are attributed to optimizations in our annotation method during sequence labeling and enhanced accuracy in identifying entity categories. This underscores that effectively addressing the complexity of increasing named entity categories in nested entities can notably boost model performance. Additionally, our model retains the simplicity and efficiency of the original method. Furthermore, our model shows notable improvements compared to the second seq2 seq-based model framework proposed by [PERSON] et al. [9]  
The comparison results in Table 14 indicate that our enhanced model achieved significant improvements while maintaining the original framework, where \"#\" denotes the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **F1 Score** \\ \hline BERT-CRF * & - & - & 64.41 \\ RICON [37] & 66.25 & 64.89 & 65.57 \\ Global Pointer [25] & - & - & 66.54 \\ \hline PNER I (ours) & 66.97 & 63.94 & 65.42 \\ PNER II (ours) & 67.53 & 65.91 & 66.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of results with existing approaches for CMeeE dataset. baseline model before improvement. We then compared our model with various benchmark models on the GENIA dataset. These benchmarks include the sequence labeling-based method by [PERSON] et al. [16] used for nested entity recognition using stacked flat entity layers, hypergraph-based approaches by [PERSON] and [PERSON] [38], seq2 seq framework-based methods by [PERSON] et al. [9] and [PERSON] et al. [39], span-based methods by [PERSON] et al. [24], and span-level graphs introduced by [PERSON] et al. [40]. The results of these comparisons are presented in Table 15. From this analysis, we observe that, although our model does not outperform all benchmark models on the GENIA dataset, it is notable for its simplicity and ease of implementation while still being competitive in performance. In Table 16, we compare our model against benchmarks on the GermEval 2014 dataset, a German language dataset. For this experiment, we utilized the multilingual version of BERT-base to obtain contextual word vectors. The results indicate that our model performs better on the GermEval 2014 dataset compared to other models. Table 17 displays the execution times of the model on the GENIA dataset, calculated per single epoch round. It is observable that, due to the pipeline method requiring the use of two models concurrently, the model takes longer to execute compared to traditional sequence labeling models. However, we believe that, due to the inherently simple framework of the model, this method does not take significantly longer compared to other, more complex models. It is important to highlight that, while addressing nested named entities often leads to more complex models, the sequence labeling method remains the most traditional and straightforward approach in named entity recognition. This method requires only simple annotations for identifying entities. Our proposed method for fusing label layers represents a straightforward enhancement to this labeling technique, facilitating the recognition of nested named entities.