Abstract: Support policies can help enterprises obtain government support in funding subsidies, tax reductions, and other aspects, and help enterprises develop better. In order to address the problem that the entity boundaries in support policy texts are difficult to define and traditional word vectors cannot solve the problem of polysemy, a support policy texts named entity recognition model based on RoBERTa-wwm-BiLSTM-CRF is proposed. Firstly, the model uses the pre-trained language model RoBERTa-wwm to obtain dynamic word vectors, which can represent the polysemy of words. Secondly, the BiLSTM network is used to further extract the context information and semantic features of support policy texts. Finally, the best prediction sequence is obtained through the conditional random field. The proposed model achieves an F1 value of 91.7% on a self-built support policy dataset composed of 5 512 sentences. The results show that the model can effectively recognize the named entities in support policy texts, thereby improving the efficiency of enterprise policy screening.

Key words: support policy text, pre-trained language model, named entity recognition, dynamic word vector, enterprise support