Agricultural text named entity recognition based on the BiLSTM-CRF model
Yongqiang Qian, Xiaojin Chen, Yaojun Wang, Jingbo Zhao, Di Ouyang, Shihao Dong, Lan Huang · 2023
Named entity recognition (NER) is an essential part of many natural language processing applications and serves as the basis of natural language processing. NER tasks aim to identify the entity boundaries and types of specific meanings in texts, which include person names, place names, organizations, and dates. Due to the complexity of the composition of agricultural entities and limited quantity of agricultural corpora, often with low quality, agricultural text named entity recognition (ANER) is a more difficult and challenging problem compared to general NER tasks. Existing models often have poor performance in ANER tasks. To solve the existing problems of ANER, we constructed a named entity dataset of agricultural context through manual annotation using collected massive agricultural texts. The dataset contains 8873 entities composed of six entity categories: crop type, disease type, pest type, pesticide type, fertilizer type, and crop part type. We propose an agricultural text named entity recognition model based on BiLSTM-CRF. Experimental results show that the F1 score of the model on the test set achieved 91.20%, and our model also achieved the highest precision of 93.23% and the highest recall of 89.39% compared to other existing models.