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

TL;DR

This paper introduces a novel method for recognizing intellectual property entities in patent texts by combining Transformer models with technical word information and IDCNN-enhanced word vectors, leading to improved accuracy.

Contribution

It proposes a new approach integrating Transformer, IDCNN, and technical word information for more effective patent entity recognition, addressing limitations of existing methods.

Findings

Improved entity recognition accuracy on public datasets.

Effective use of technical word information enhances semantic understanding.

Transformer with relative position encoding captures deep semantic features.

Abstract

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.