Abstract:

[Application Background] Agricultural science and technology policies have a significant impact on technological progress and the development of agricultural production. Policies issued by different government departments have correlations with conceptual entities. [Objective] Addressing the issue of time-consuming and labor-intensive manual feature design for named entity recognition and relationship extraction in agricultural science and technology policies, this study introduces a knowledge extraction approach utilizing the BERT-BiLSTM-CRF model. [Method] Using a new annotation pattern adapted to the domain corpus, directly modeling triplets, instead of the traditional separate modeling or joint extraction, transforms the entity and relationship extraction problem into a sequence labeling task. The experiment involved 19,779 sentences and 376,721 characters of policy text, identifying eight types of entities such as policy and industry, and ten types of relationships such as citation and publication. [Results] The model achieves an accuracy of 81.61%, a recall of 85.34%, and an F1 score of 83.47% on the corpus. The results of the experiments demonstrate that the suggested approach proficiently extracts entities and relationships related to agricultural science and technology policies, and its performance surpasses that of other classical models.

Key words: agricultural science and technology policies, BERT-BiLSTM-CRF, knowledge extraction, entity recognition