# Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition
## 5 Experimental Results and Analysis
### Discussion  
BiLSTM-CRF consists of an embedding layer, a BiLSTM layer, and a CRF layer, which can extract high features of the text. The embedding layer can encode Chinese characters or words and quantify the text. The output of BiLSTM can also be used as a predictive label, but there are still some problems. For example, labels that are not adjacent may be predicted to be adjacent. CRF is a sequential labeling model, which considers the order and correlation between labels. The association between states can be learned through the feature function. CRF layer has the characteristics of integrating context labels. The introduction of CRF after the output of the BiLSTM layer can avoid incorrect tag output and make the output more accurate and reasonable. The BERT-CRF model includes the BERT layer and CRF layer. Based on a multi-layer bidirectional transformer encoder, BERT [30] is a pre-trained unsupervised natural language processing modelBERT is, which solves the problem of word polysemy. Transformer uses a two-way self-attention mechanism instead of one-way integration of context information. BERT uses a pre-trained masked language model (MLM) and the deep bidirectional transformer component, which generates a deep bidirectional linguistic representation of the integrated context information. BERT converts the input text into a one-dimensional vector containing a word vector, a text vector, and a position vector as the model input and outputs the one-dimensional word vector corresponding to each character as the semantic representation of the text. The pre-training BERT can encode the annotation data and be trained to obtain the accurate semantic representation of characters. CRF performs a state transition constraint on the output result of the upper layer. GlobalPointer is mainly used to solve multi-label classification. Global normalization is used to identify named entities, nested entities, and non-nested entities indiscriminately. If there are m entity types to be identified, then there are m multi-label classification problems. GlobalPointer is simple and efficient without recursively calculating the denominator when training or dynamic programming when predicting. Ideally, the time complexity is O (1). In order to solve the problem of inconsistent training and prediction caused by recognizing the head and tail of an entity, GlobalPointer regards the head and tail as a whole. Introducing IDCNN to the combination of BERT-CRF and BiLSTM-CRF, BERT-BiLSTM-IDCNN-CRF can be obtained. The BERT layer converts the sentence into a vector representation. The BiLSTM layer obtains all the information of a sequence through the front and back directions. The IDCNN layer extracts the features of words and sentences, and the CRF layer obtains the final prediction results. The RoBERTa-BiLSTM-IDCNN-CRF model can be obtained by changing the pre-trained model in the BERT-BiLSTM-IDCNN-CRF model to RoBERTa. RoBERTa [31] contains a transformer encoder which has powerful feature extraction capabilities. Its core part, the self-attention mechanism, can model the relationship between words of any length and captures the grammatical structure within a sentence and the dependencies between words. RoBERTa learns the dynamic word vector representation by referring to the context of words, which solves the problem that the static word vector cannot represent polysemous words and improves the word representation ability. Based on BiLSTM-CRF, IDCNN is introduced to obtain the BiLSTM-IDCNN-CRF model. The model first uses BiLSTM-IDCNN to extract features from text information and then uses the CRF layer to classify each character or word. In order to further understand the task of the Chinese pesticide NER, recognition errors were analyzed. Table 4 shows the extraction results of BiLSTM-IDCNN-CRF for 10 types of entities. BiLSTM-IDCNN-CRF achieves different results in the F1 score.