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Related Experiment Video

Updated: Oct 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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English Grammar Detection Based on LSTM-CRF Machine Learning Model.

Liqin Wu1, Meisen Pan2

  • 1School of International Education, Hunan University of Medicine, Huaihua, Hunan 418000, China.

Computational Intelligence and Neuroscience
|August 30, 2021
PubMed
Summary

This study introduces an advanced machine learning model for English grammar detection. The Long Short-Term Memory-Conditional Random Field (LSTM-CRF) model enhances accuracy and simplifies the analysis process.

Related Experiment Videos

Last Updated: Oct 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Machine Learning

Background:

  • Deep learning and neural networks excel in various information processing tasks.
  • English grammar accuracy and standardization are crucial for natural language applications.
  • Traditional machine learning models often rely on manual feature selection.

Purpose of the Study:

  • To propose a machine learning model for accurate English grammar detection and analysis.
  • To address limitations of traditional models in feature selection for grammar detection.
  • To improve the efficiency and accuracy of English grammar recognition systems.

Main Methods:

  • Utilized Long Short-Term Memory (LSTM) and Conditional Random Field (CRF) network models.
  • Developed a grammar database based on English morphological features and word segmentation rules.
  • Designed a radial basis function neural network structure for grammar semantic detection.

Main Results:

  • The proposed LSTM-CRF model effectively recognizes English grammar text entities.
  • The system simplifies the recognition process structure and reduces operational cycles.
  • Overall detection accuracy for English grammar is significantly improved.

Conclusions:

  • The LSTM-CRF model offers a robust solution for English grammar detection.
  • This approach enhances accuracy and efficiency compared to traditional methods.
  • The model simplifies the grammar detection pipeline, making it more practical.