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.