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.

本文提出了一种基于Transformer和技术词信息的知识产权实体抽取方法，并结合BERT语言方法提供准确的词向量表示。该方法通过引入相对位置编码的Transformer编码器，可以从词向量序列中学习文本的深层语义信息，并实现实体标签预测，从而提高了实体识别的准确性。

一种基于Transformer和技术词信息的知识产权实体识别方法