Optimizing Relation Extraction Based on the Type Tag of Named Entity

Zhang, Yixing; Zhang, Yangsen; Huang, Gaijuan; Guo, Zhengbin

doi:10.1007/978-3-030-04015-4_45

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been several proposed relation extraction techniques in Chinese text 4 , however, they have not been directly applied to the CI.…”

Section: Introductionmentioning

confidence: 99%

A people-item relation extraction method based on multiple kernel support vector machine model

Gao,

Liu

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

View full text Add to dashboard Cite

In this research, we present a novel approach for relation extraction using the multiple kernel support vector machine model. The aim is to improve the comprehension of Chinese Instructions by family service robots. Our approach focuses on extracting the people-item relation from Chinese instructions. We start by defining four categories of people-item relations: sequential, belong to, equivalent, and direction. Next, we construct a feature combination for the entity using lexical, phrase, order, and property features. We then generate multiple kernel functions using a weighted sum method and selected foundation kernel functions (lexical, syntactic, and property). The multiple kernel support vector machine model is constructed using a simple multiple kernel learning technique. The experimental findings validate that our proposed approach outperforms current methodologies in terms of accuracy, retrieval rate, and F-measure.

show abstract

“…There have been several proposed relation extraction techniques in Chinese text 4 , however, they have not been directly applied to the CI.…”

Section: Introductionmentioning

confidence: 99%