# A Multi-Granularity Word Fusion Method for Chinese NER
## 4 Experiments
### Results
#### 4.5.3 Comparison with baselines  
Table 2 shows the experimental results (F1 scores) of the baselines and our method on the four datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Ontonotes4.0** & **MSRA** & **Resume** & **Weibo** \\ \hline BiLSTM-CRF & 71.81 & 91.87 & 94.41 & 56.75 \\ Lattice LSTM & 73.88 & 93.18 & 94.46 & 58.79 \\ FLAT & 76.45 & 94.35 & 95.45 & 63.42 \\ MECT & 76.92 & 94.32 & 95.89 & 63.30 \\ \hline BERT & 80.14 & 94.95 & 94.87 & 68.20 \\ RoBERTa & 81.39 & 95.38 & 95.31 & 68.35 \\ ZEN & - & 95.25 & - & - \\ \hline PLTE & 80.60 & 94.53 & 96.45 & 69.23 \\ MW-NER & 81.51 & 95.47 & 95.87 & 69.41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of methods on four datasets (%).  
Figure 4: F1 scores of Different Fusion Strategies on Resume (%).  
Figure 3: F1 scores of Different Tokenizers on Resume (%).  
Our model outperforms both BiLSTM-CRF and Lattice LSTM in terms of performance. On the Resume, Ontonotes, and MSRA datasets, our model outperforms FLAT by 0.42%, 5.06%, and 1.12%, respectively. The improvement on the Weibo dataset is even greater, reaching 5.99%. And when compared to MECT, our model trails by 0.02 percent on the Resume dataset while leading on the other datasets. This could be due to the benefits of multi-layer Transformers and the evolution of multi-granularity word information fusion. In comparison with [PERSON] and RoBERTa, our model improves on each dataset, with the biggest gap reaching 1.37%. This demonstrates that combining moderate and weak information could indeed enhance the performance of our model.  
Compared with PLTE, a novel model with currently excellent results, our model's F1 scores on the Ontonotes, MSRA, and Weibo datasets are higher. However, there is still room for improvement in the Resume dataset, as our model's F1 score lags behind that of PLTE. In the Resume dataset, we suspect our model is inferior to PLTE for the following reasons. PLTE introduces a porous mechanism to augment localness modeling and maintain the strength of capturing the rich long-term dependencies. Our model introduces richer word information, and uses Bi-LSTM to model long-term dependencies. However, the effect of Bi-LSTM may be inferior to that of the porous mechanism. Furthermore, due to the small size of the Resume dataset, our model could not be fully trained.  
According to experiments on Chinese NER benchmark datasets, our method outperforms most baselines and achieves the state-of-the-art performance. We consider that there are two main reasons for the excellent results of our model. On the one hand, our model is inherently based on BERT, which makes the character embeddings richer and more dynamic to learn appropriate semantics. On the other hand, our model focuses on word information, and uses multi-granularity word information back to guide character information. In other words, the strong-weak feedback attention mechanism plays a crucial role. Due to powerful character information and richer word information, our model presents better performance. However, some modules of our model might not take full advantage of the parallel power of the GPU. In the strong-weak feedback attention mechanism, the advantage of parallel computing may not be fully utilized because one character in a sentence might be fused with many words or phrases in corresponding positions. So, the computational speed of the model is impaired. We are aware of this issue, and will study it in our future work.