Research on named entity recognition in natural environment based on deep learning
Hong Fang, Lan Zhang · 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI) · 2022
The extraction of named entities and relationships is of great significance in the field of natural environment. However, Chinese natural environment entity corpus inventory is blank, which brings great challenges to the downstream task of natural language processing in this field. In order to study and improve the effect of natural environment data named entity recognition, an improved CNN-BiGRU-CRF model with attention mechanism is proposed. The constructed natural environment entity corpus was used as experimental data and labeled with BIOE sequence labeling method for comparative experiments. The experimental results show that the F1 value of six types of entity recognition reaches more than 80%, which is better than other traditional models and achieves better recognition effect on natural environment data.