Simulation of English Word Order Sorting Based on Semionline Model and Artificial Intelligence

Area of Science:

• Information Retrieval
• Artificial Intelligence
• Machine Learning

Background:

• Improving word order ranking is crucial for effective English language retrieval.
• Existing models may not fully capture the nuances of word order in retrieval tasks.

Purpose of the Study:

• To develop an artificial intelligence (AI) ranking model for English word order retrieval.
• To enhance the effectiveness of English language retrieval through improved word order ranking.

Main Methods:

• Constructed an AI ranking model using machine learning and a semisupervised extreme learning machine (ELM) regression model.
• Employed Fuzzy C-Means (FCM) clustering for sample screening and ELM collaborative training for sample labeling.
• Utilized continuous learning with OSELMR and confidence evaluation for unlabeled sample selection and SSOSELMR weight calculation.

Main Results:

• The semisupervised ELM regression model was mathematically derived and implemented.
• Collaborative training and confidence evaluation effectively utilized labeled and unlabeled data.
• Control experiments demonstrated the statistical effectiveness of the proposed model.

Conclusions:

• The developed AI ranking model based on semisupervised ELM is effective for English word order retrieval.
• The integration of machine learning and semisupervised techniques significantly improves ranking performance.
• The study provides a robust framework for enhancing information retrieval systems.