# Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition
###### Abstract  
Chinese pesticide named-entity recognition (NER) aims to identify named entities related to pesticide properties from unstructured Chinese pesticide information texts. In view of the characteristics of massive, fragmented, professional, and complex semantic relationships of pesticide information data, a deep learning method based on multi-feature fusion was applied to improve the accuracy of pesticide NER. In this study, the pesticide data set is manually annotated by the begin inside outside (BIO) sequence annotation scheme. Bi-directional long short-term memory (BiLSTM) and iterated dilated convolutional neural networks (IDCNN) combined with conditional random field (CRF) form the model BiLSTM-IDCNN-CRF, and it is applied to implement named-entity recognition in Chinese pesticide data sets. IDCNN is introduced to enhance the semantic representation ability and local feature capture ability of the text. BiLSTM network and IDCNN network are combined to obtain the long-distance dependence relationship and context features of different granularity of pesticide data text. Finally, CRF is used to implement the sequence labeling task. According to the experiment results, the accuracy rate, recall rate, and F1 score of the BiLSTM-IDCNN-CRF model in the Chinese pesticide data set were 78.59%, 68.71%, and 73.32%, respectively, which are significantly better than other compared models. Experiments show that the BiLSTM-IDCNN-CRF model can effectively identify and extract entities from Chinese pesticide information text data, which is helpful in constructing the pesticide information knowledge graph and intelligent question-answering. pesticide named-entity recognition (NER); multi-feature fusion; BiLSTM-IDCNN-CRF +
Footnote †: journal: Medical  
work, help farmers use pesticides scientifically, reasonably and safely, and promote the application of text extraction technology in agricultural data analysis. The research on named-entity recognition (NER) in the general field has been relatively mature. NER has the characteristics of a relatively stable number, standardized structure, and unified naming rules. In the field of agricultural information, NER in agricultural planting, agricultural pests, and diseases [1; 2; 3; 4; 5] has been developed gradually in recent years. In the field of Chinese pesticide, there are many kinds of named entities with distinct domain particularity, and a pesticide may have many aliases, or the same pesticide name has completely different semantics in different texts [6]. Pesticide NER is full of challenges because of the complex structure of Chinese pesticide information text. Aiming at the Chinese pesticide NER, a BiLSTM-IDCNN-CRF model with multi-feature fusion is applied in this study, which combines the BiLSTM model and IDCNN model to extract features from texts with different granularities, and then integrates the features obtained by these two models and introduces the CRF layer for classification. Compared with a single model, the model applied in this paper can make full use of text information for Chinese pesticide entity recognition. Experiments show that the applied method in this paper achieves better results than BERT, RoBERTa, BiLSTM, IDCNN, CRF, GlobalPointer, and other models on pesticide data sets. The main contributions of this study are as follows: Firstly, the Chinese pesticide technical field data set was constructed by preprocessing, and BIO sequence labeling was carried out. Secondly, a multi-model fusion model BiLSTM-IDCNN-CRF is applied to Chinese pesticide naming entity recognition: BiLSTM mechanism makes the model pay attention to the global context features, which makes the text features more accurate. The IDCNN model has the advantage of taking into account local features. The text features with different granularity extracted with the BiLSTM model and IDCNN model are fused.