An intelligent question-answering of agricultural knowledge can be one of the most important parts of information agriculture. Among them, named entity recognition has been a key technology for intelligent question-answering and knowledge graph construction in the fields of agricultural domain. It is also a high demand for the accurate identification of named entities. Furthermore, the Chinese named entity recognition can be confined to the location and semantic information of characters, due to the long length of agricultural entity and complex naming. Therefore, it is very necessary to improve the recognition performance in the process of named entity recognition, particularly for the sufficient capture of character position, contextual semantic features, and long-distance dependency information. In this study, a novel Chinese named entity recognition of agriculture was proposed using EmBERT-BiLSTM-CRF model. Firstly, the Bidirectional Encoder Representation from Transformers (BERT) pre-trained language model was applied as the layer of word embedding. The context semantic representation of the model was then improved to alleviate the polysemy, when pre-training the depth bidirectional representation of word vectors. Secondly, the language masking of BERT was enhanced significantly, according to the characteristics of Chinese. An Entity-level Masking strategy was utilized to completely mask the Chinese entities in the sentence with the consecutive tokens. The Chinese semantics was then better represented to alleviate the bias caused by incomplete semantics. Thirdly, the Bidirectional Long Short-Term Memory Network (BiLSTM) model was adopted to learn the semantic features of long-sequence using two LSTM networks (forward and backward), considering the contextual information in both directions at the same time. The long-distance dependency information of text was then captured during this time. Finally, the Conditional Random Field (CRF) was used to learn the labelling constraint in the training data. Among them, the learned constraint rules were used to detect whether the label sequence was legal during prediction. After that, the CRF also utilized the information of adjacent labels to output the globally optimal label sequence. Thus, the output of the model was a dependent label sequence, but an optimal sequence was considered the rules and order. A focal loss function was also used to alleviate the unbalanced sample distribution. A series of experiments were performed to construct the corpus of named entity recognition. As such, the corpus contained a total of 29 790 agricultural entities after BIO labelling, including 11 057 crops, 8 121 pesticides, 4 505 diseases, and 6 107 pest entities, in which the training, validation, and test set were divided, according to the ratio of 7:2:1. Four types of agricultural entities from the text were identified, including the crop varieties, pesticides, diseases, and insect pests, and then to label them. The experimental results show that the recognition accuracy of the EmBERT-BiLSTM-CRF model for the four types of entities was 94.97%, and the F1 score was 95.93%. Which compared with the models based on BiLSTM-CRF and BERT-BiLSTM-CRF, the recognition performance of EmBERT-BiLSTM-CRF is significantly improved, proved that used pre-trained language model as the a word embedding layer can represent the characteristics of characters well and the Entity-level Masking strategy can alleviate the bias caused by incomplete semantics, thereby enhanced the Chinese semantic representation ability of the model, so that enabling the model to more accurately identify Chinese agricultural named entities. This research can not only provide arelatively high entity recognition accuracy for tasks such as agricultural intelligence question answering, but also offer new ideas for the identification of Chinese named entities in fishery, animal husbandry, Chinese medical, and biological fields. © 2022 Chinese Society of Agricultural Engineering. All rights reserved.
