Abstract: In the information age,the volume of data in the equipment domain has surged dramatically,making it challenging for analysts to efficientlyextract critical information to support relevant data analyses and arguments. To address the issue of ambi-guous event argument boundaries in the extraction of events within the equipment sector,a semantic-enhanced event extraction method is proposed. This method utilizes specialized terminology and vocabulary information in the equipment domain to construct domain word vectors,and designs a model structure that can be compatible with and integrate semantic information of different granularity,fuses the equipment domain word vectors with character vectors generated by ERNIE of the pre-trained mo-del,combines the knowledge of specialized terminology with the ability of general language comprehension,and realizes a more comprehensive capturing of semantic information that enhances the model’s understanding of the textual semantics of the equipment domain,so as to improve the model’s ability to recognize the boundaries of the event thesis elements.Experimental results demonstrate that,on the equipment domain dataset,the proposed method’s F1 values outperforms baseline approaches,with an improvement of 3.83% compared to the CK-BERT model,and has been validated on the public dataset ACE2005,thereby effectively improving performance in the extraction of event elements in the equipment domain.

Key words: Equipment domain, Event extraction, Semantic enhancement, Domain-specific word vector, Pre-trained model