# Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition
## 5 Experimental Results and Analysis
### Analysis of Experimental Results  
In order to verify the applied BiLSTM-IDCNN-CRF model on the pesticide data set, BERT-CRF, BiLSTM-CRF, GlobalPointer, RoBERTa-BiLSTM-IDCNN-CRF, and BERT-BiLSTM- IDCNN-CRF are introduced in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data \({}^{*}\)** & **Label** & **Data \({}^{*}\)** & **Label** \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ **Net** \\ \hline \hline \end{tabular}  &
\begin{tabular}{c} B-NAME \\ **Net** \\Although the existing research has used the BiLSTM-IDCNN-CRF to perform the named-entity recognition task, the research on the named-entity recognition of Chinese pesticide data is still blank. In our study, the hyperparameters are adaptively tuned based on the performance of the model on the dataset in the process of modeling. The training framework of model BiLSTM-IDCNN-CRF uses tensorflow-gpu 2.3.0, tensorflow-addons 0.15.0, and transformers 4.6.1. The embedding dim is set to 300, hidden dim is 200, filter nums is 64, IDCNN nums is 2, maxlen is 512, and dropout is 0.3. The learning rate is \(10^{-5}\). The training framework of model BiLSTM-CRF uses torch 1.8. The maxlen is set to 512, dropout is 0.5, embed size is 256, and hidden size is 256. BERT-CRF is trained with tensorflow 1.14.0, bert4 keras 0.11.4, and keras 2.3.1 frameworks. The maxlen is set to 512, Bert layers are 12, the learning rate is \(2^{-5}\), and the batch size is 16. GlobalPointer uses the same training framework and parameters as BERT-CRF. RoBERTa-BiLSTM-IDCNN-CRF and BERT-BiLSTM-IDCNN-CRF take the same training framework and parameters as BiLSTM- IDCNN-CRF. Experiment results of named-entity recognition based on these different models are shown in Table 3.  
For the combination of the pre-training BERT and CRF, the identification precision, recall, accuracy, and F1 score of the BERT-CRF model on pesticide data sets are only 75%, 56%, 41.58%, 79.98%, and 53. 64%. BiLSTM-CRF avoids the problem of introducing noise in the data set; as such, it can effectively learn the features of the text and implement NER by combining context information and word segmentation information, and the recall rate and accuracy rate are obviously improved compared with the BERT-CRF model, so the comprehensive evaluation index F1 score is improved by 5.75%. GlobalPointer globally normalizes the pesticide data set, distinguishes nested entities from non-nested entities, and improves the F1 score by 6. 61% compared with BiLSTM-CRF. Based on the model BiLSTM-CRF, RoBERTa-BiLSTM-IDCNN-CRF introduces a robustly optimized bert (RoBERTa) and IDCNN, solves the problems of polysemy and long-distance dependency by learning the dynamic word vector representation, and improves the word representation ability. Compared with the BiLSTM-CRF, the precision, recall, precision, and F1 score of BiLSTM-IDCNN-CRF are improved by 9. 43%, 14.64%, 2.22%, and 12. 31%, respectively. The F1 score of BERT-BiLSTM-IDCNN-CRF is 1.2% higher than that of RoBERTa-BiLSTM-IDCNN-CRF. Experimental results show that BERT is better than RoBERTa. With a strong semantic representation ability, BERT combined with BiLSTM and IDCNN can capture the context features while taking into account the local features, and multi-semantic feature fusion has a great improvement on the pesticide entity recognition effect. Compared with the experimental results of BiLSTM-CNN-CRF, the performance of BiLSTM-IDCNN-CRF is better; specifically, the F1 score is nearly 10% higher, which indicates that the local feature modeling ability of IDCNN is better than that of the traditional CNN model in Chinese pesticide data.