# A Chinese Named Entity Recognition Method Based on ERNIE-BiLSTM-CRF for Food Safety Domain
## 4 Experiments
### Experimental Results  
The results of the experiments on the datasets Weibo NER and Food are shown in Tables 3 and 4. Comparisons are made with other SOTA models on the Weibo NER dataset and some baseline models on the Food dataset.  
The ERNIE-Adv-BiLSTM-Att-CRF model proposed in this paper takes ERNIE-BiLSTM-CRF as the baseline and adds adversarial training and self-attention. In the SOTA model shown in Table 3:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Food**} \\  & **P** & **R** & **F1** \\ \hline ERNIE + Softmax & 64.47 & 67.74 & 66.07 \\ ERNIE + BiLSTM + CRF & 66.37 & 69.12 & 67.72 \\ \hline Our Model & **68.44** & **70.97** & **69.68** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed statistics of our model on Food.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Weibo NER**} \\  & **P** & **R** & **F1** \\ \hline FLAT & N/A & N/A & 60.32 \\ \hline SLK-NER & 61.8 & 66.3 & 64.0 \\ \hline Locate and Label & 70.11 & 68.12 & 69.16 \\ \hline AESINER & N/A & N/A & 69.78 \\ \hline SA-NER & N/A & N/A & 69.80 \\ \hline BERT-LMCRF & 66.88 & 67.33 & 67.12 \\ \hline FLAT + BERT & N/A & N/A & 68.55 \\ \hline FGN & 69.02 & **73.65** & 71.25 \\ \hline Our Model & **72.82** & 72.46 & **72.64** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed statistics of our model on Weibo NER.  
Locate and Label uses a two-stage entity recognizer that locates entities and labels boundaries for nested NER;
* SA-NER uses semantic expansion to improve the performance of NER;
* AESINER improves the named entity recognition ability of the model by introducing extra knowledge;
* SLK-NER uses an attention mechanism to fuse lexical knowledge into character-based models;
* FLAT without the pre-trained model BERT uses Lattice information;
* BERT-LMCRF is a BERT model that uses BiLSTM-CRF as a NER tagger;
* FLAT + BERT is a SOTA model based on BERT;
* FGN is a fused glyph network based on BERT.  
Table 3 shows the data of each SOTA model are the experimental results in their original papers. Two baseline models are selected in Table 4 for comparison with the model in this paper:  
1. ERNIE-based and using softmax for entity classification;
2. ERNIE-based and using BiLSTM-CRF as the NER sequence coder-decoder.  
As shown in Table 3, our model outperforms other SOTA models on the Weibo NER dataset. Compared with the FLAT, SLK-NER, Locate and Label, and AESINER models, the F1 values obtained notable improvements of 12.32%, 8.64%, 3.48%, and 2.86%, respectively; compared with the BERT-LMCRF, FLAT + BERT, SA-NER, and FGN, they also obtained remarkable improvements in the F1 values, with 5.52%, 4.09%, 2.84%, and 1.39% improvement, respectively.  
On the Food dataset, it can be seen from Table 4 that our model is significantly higher than the other two baseline models in terms of the F1 value, which is 69.68%. The improvement is 3.61% and 1.96%, respectively. It can be seen that adding the BiLSTM-CRF network after ERNIE is better than directly classifying the output of ERNIE for prediction, with an F1 value improvement of 1.65%. After adding adversarial training to the model training process and self-attention in BiLSTM-CRF, the model is further improved with another F1 value improvement of 1.96%.  
From this, we can see that our model can alleviate the impact of noise on NER performance in Weibo NER and Food, two small-scale datasets with noise confounding. After self-attention, the features with a high impact on entity classification among all the features output by BiLSTM get greater weights, making entity recognition better.