A Patent Keyword Extraction Method Based on Corpus Classification

Changjian Sun, Wentao Chen (), Zhen Zhang and Tian Zhang
Additional contact information
Changjian Sun: Changzhou Intelllectual Property Protection Center, Changzhou 213100, China
Wentao Chen: Changzhou Intelllectual Property Protection Center, Changzhou 213100, China
Zhen Zhang: School of Physics, Northeast Normal University, Changchun 130024, China
Tian Zhang: School of Physics, Northeast Normal University, Changchun 130024, China

Mathematics, 2024, vol. 12, issue 7, 1-15

Abstract: The keyword extraction of patents is crucial for technicians to master the trends of technology. Traditional keyword extraction approaches only handle short text like title or claims, but ignore the comprehensive meaning of the description. This paper proposes a novel patent keyword extraction method based on corpus classification (PKECC), which simulates the patent understanding methods of human patent examiners. First of all, a corpus classification model based on multi-level attention mechanism adopts the Bert model and hierarchical attention mechanism to classify the sentences of patent description into four parts including technical field, technical problem, technical solution, and technical effect. Then, the proposed keyword extraction method based on the fusion of BiLSTM and CRF is incorporated to extract keywords from the four parts. The proposed PKECC simulates understanding style of patent examiner by extracting keywords from the description. Meanwhile, PKECC may reduce the complexity of extracting keywords from a long text and improve the accuracy of keyword extraction. The proposed PKECC is compared with 5 traditional or state-of-the-art models and achieves better accuracy, F1 score and recall rate; its recall rate is above 62%, its accuracy reaches over 84%, and the F1 score arrives at 69%. In addition, the experimental results shows the proposed PKECC has a better universality in keyword extraction.

Keywords: patent keyword extraction; corpus classification; BiLSTM; CRF; hierarchical attention mechanism; Bert (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/7/1068/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/7/1068/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:7:p:1068-:d:1368996

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().