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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Intelligent evaluation of English writing is a growing challenge.
  • Existing semantic analysis systems require enhancement for accuracy and efficiency.

Purpose of the Study:

  • To propose and evaluate an English semantic neural network algorithm for intelligent English writing evaluation.
  • To improve the accuracy and efficiency of English translation systems.

Main Methods:

  • Analysis of English semantic analysis systems.
  • Exploration of distance similarity algorithms, semantic analysis structures, and word/sentence analysis algorithms.
  • Implementation of a neural network algorithm for semantic analysis.
  • Database and method implementation for the English semantic analysis system.

Main Results:

  • The proposed BRF network achieved 96.35% recognition accuracy for English characters, surpassing the BP network by 7.79%.
  • The BRF network demonstrated a higher AUC of 0.89 compared to the BP network's 0.72.
  • Experimental results align with antinoise curve tests, validating the algorithm's robustness.

Conclusions:

  • The English semantic neural network algorithm effectively improves English translation accuracy.
  • The proposed method enhances overall system efficiency for English writing evaluation.