  Patent texts contain a large amount of entity information. Through named
entity recognition, intellectual property entity information containing key
information can be extracted from it, helping researchers to understand the
patent content faster. Therefore, it is difficult for existing named entity
extraction methods to make full use of the semantic information at the word
level brought about by professional vocabulary changes. This paper proposes a
method for extracting intellectual property entities based on Transformer and
technical word information , and provides accurate word vector representation
in combination with the BERT language method. In the process of word vector
generation, the technical word information extracted by IDCNN is added to
improve the understanding of intellectual property entities Representation
ability. Finally, the Transformer encoder that introduces relative position
encoding is used to learn the deep semantic information of the text from the
sequence of word vectors, and realize entity label prediction. Experimental
results on public datasets and annotated patent datasets show that the method
improves the accuracy of entity recognition.
