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Patent texts contain a large amount of entity information. Through namedentity recognition, intellectual property entity information containing keyinformation can be extracted from it, helping researchers to understand thepatent content faster. Therefore, it is difficult for existing named entityextraction methods to make full use of the semantic information at the wordlevel brought about by professional vocabulary changes. This paper proposes amethod for extracting intellectual property entities based on Transformer andtechnical word information , and provides accurate word vector representationin combination with the BERT language method. In the process of word vectorgeneration, the technical word information extracted by IDCNN is added toimprove the understanding of intellectual property entities Representationability. Finally, the Transformer encoder that introduces relative positionencoding is used to learn the deep semantic information of the text from thesequence of word vectors, and realize entity label prediction. Experimentalresults on public datasets and annotated patent datasets show that the methodimproves the accuracy of entity recognition.

An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information