# Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition
## 2 Related Work
Iterated dilated convolutional neural network (IDCNN) [14; 15] is composed of multiple layers of different dilated convolutional neural networks (DCNN) [16]. Since each word in the input sentence may have an impact on the labeling of the current position, the last layer of neurons may not be able to capture the information of the entire input sentence, and only part of the raw data can be obtained. IDCNN can extract local features while paying attention to long-distance information; hence, it solves the long-distance dependence problem that the convolutional neural network CNN [17; 18; 19] cannot solve. [PERSON] et al. [20] introduced the LSTM model in sequence labeling and proposed for the first time to combine the bidirectional long and short-memory neural network mode with the CRF layer for the task of NER. For the BiLSTM-CRF model, the bidirectional LSTM component can effectively use the past and future input features, while the CRF layer can use the sentence-level tag information. Experiments show that the BiLSTM-CRF model can make full use of future input features without the help of word embedding and can produce accurate labeling performance with robustness. [PERSON] et al. [21] introduced a neural network framework combining BiLSTM, CNN, and CRF, which can extract deep features from the text at the word and character level. The model does not need feature engineering and data preprocessing, whereas the traditional sequence labeling system requires a lot of task-specific knowledge in the form of manual features and data preprocessing. [PERSON] et al. [22] proposed a Chinese NER model combining multi-scale local context features and self-attention mechanism for agricultural pest and disease texts, constructed a JMCA-ADP model based on BiLSTM-CRF, and established an agricultural pest and disease available corpus AgCNER. Compared with similar corpus, this corpus has more categories and a larger sample size, which deals with the problem of ignoring the potential local context features of the text. [PERSON] et al. [23] proposed a BiLSTM-IDCNN-CRF model based on word embedding, which combines the characteristics of different granularities obtained by the BiLSTM network and IDCNN network, and each output of dilated convolutional layer IDCNN contains a wide range of information without the loss information of pooling. The model has obtained good results in the ecological NER of the ecological governance technology data set. [PERSON] et al. [24] compared and analyzed the experimental effects of BERT, RoBERTa, ALBERT, and ELECTRA. Experiments show that the RoBERTa-MIMIC model achieves the most advanced performance on three public clinical concept extraction datasets and verifies the excellent performance of the RoBERTa model in medical named-entity recognition tasks. [PERSON] et al. [25] compared the performance of NER based on CRF, LSTM, LSTM-CRF, and BERT around intangible cultural heritage with traditional music. Experiments show that BERT has the best semantic representation ability among the four models and can better complete the task of automatic recognition of traditional music terms, but it has the limitation of only recognizing some unique terms and having a small training set. Compared with the general corpus, the information density of Chinese pesticide text is higher, which contains a large number of specific terms related to the fields of animal, botany, and chemistry, and there are widespread phenomena such as long-distance dependence, bidirectional dependence, the coexistence of normative expressions and abbreviated colloquial expressions and terminology nested. Some existing modules integrating multiple neural networks have complex structures, and some works easily cause errors in the identification of pesticide entity boundaries and categories. The granularity of the extracted entities is relatively rough, and the semantic features of the obtained text are insufficient.