Method Entity and Relation Extraction Based on Automatically Generated Syntactic Templates: A Case Study of CSDN Artificial Intelligence Blog

Authors

• Kuiliang LI, Department of Information Management, Peking University, Beijing 100871
• Bolin HUANG, Department of Information Management, Peking University, Beijing 100871

Keywords

entity relation Extraction; method entity; automatic generation; syntactic template; seed learning

Abstract

[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using a filter model. [Result/conclusion]After 5 rounds of iterative extraction, the triplet extraction accuracy of the model reaches 55.2%, which is better than the existing general model. The results show that this method can effectively use the limited labeled data to extract method entities and their relationships in specific fields, and provide support for scientific and technological information analysis in academia and industry.

First Page

30

Last Page

40

Submission Date

09-Aug-2024

Revision Date

09-Oct-2024

Acceptance Date

13-Nov-2024

Published Date

01-Jan-2025

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Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2025.01.003

Recommended Citation

LI, Kuiliang and HUANG, Bolin (2025) "Method Entity and Relation Extraction Based on Automatically Generated Syntactic Templates: A Case Study of CSDN Artificial Intelligence Blog," Journal of Scientific Information Research: Vol. 7: Iss. 1, Article 3.
DOI: 10.19809/j.cnki.kjqbyj.2025.01.003
Available at: https://eng.kjqbyj.com/journal/vol7/iss1/3