Abstract: The preparation and review of bidding documents play a crucial role in ensuring the smooth operation of the bidding process.Entity recognition technology can notably enhance the accuracy and efficiency of information extraction,thereby improving the readability and retrievability of information during the review of bidding documents.However,due to the complexity of the content and the presence of numerous specialized terms,recognizing long entities poses a significant challenge.Traditional methods for named entity recognition(NER) perform poorly in addressing these issues.This paper proposes an NER approach named Roberta-DFF-BiLSTM-MHA-CRF,which integrates a multi-head attention mechanism,dictionary feature fusion,and the Roberta-BiLSTM-CRF model.Utilizing Roberta as the input layer,this method enhances the capability to capture long-range dependencies.The introduction of the multi-head self-attention mechanism improves the recognition of long entities.Meanwhile,incorporating domain-specific dictionary features addresses the issue of unclear term boundaries.Experimental results demonstrate that the proposed model significantly boosts the accuracy and efficiency of information extraction in the context of NER for bidding documents.When compared to the Bert-BiLSTM-CRF model,it achieves a 2.49 percentage point improvement in precision,a 4.28 percentage point increase in recall,and a 3.37 percentage point enhancement in F1 score.These improvements effectively reduce time and labor costs,offering an efficient new solution for information extraction from bidding documents.

Key words: Entity recognition in tender documents, Multi-head attention, Dictionary feature fusion, Roberta