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

Abstract

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.

Enhancement of Semantic Analysis Based on the Ontology Reengineering: A Case Study on Moroccan Tourism Domain

“…Relationship extraction is an essential aspect of information extraction, aiming to discern the semantic relationship between pairs of entities presented in natural language text. These entities may be linked explicitly or implicitly (Gao and Liu, 2023). Relationship extraction is of paramount importance in diverse applications and its techniques have been widely applied in knowledge graphs, question-and-answer systems, information retrieval, intelligent customer service and various other fields (Zhu et al, 2023).…”