Research on named entity recognition method of marine natural products based on attention mechanism - Ma, Xiaodong; Yu, Rilei; Gao, Chunxiao; Wei, Zhiqiang; Xia, Yimin; Wang, Xiaowei; Liu, Hao (Frontiers in Chemistry, 2023)
Marine natural product entity property information is the basis of marine drug development, and these entity property information can be obtained from the original literature. However, the traditional methods require a lot of manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be well solved. In order to solve the above problems, this paper proposes a named entity recognition method based on attention mechanism, inflated convolutional neural network ( IDCNN ) and conditional random field ( CRF ), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of inflated convolutional neural network to parallelize operations and long and short-term memory, and the excellent learning ability, a named entity recognition algorithm model for automatic recognition of entity information in marine natural product domain literature is developed. Experiments demonstrate that the proposed model is able to identify entity information from unstructured chapter-level literature well and outperforms the control model in several metrics. In addition, we construct an unstructured text dataset related to marine natural products from an open source dataset, which can be used for research and development of resource scarcity scenarios.