Semantic analysis-integrated LSTM neural network: a novel spelling correction approach for academic English translation

Key Points

• This research aims to develop a new method for spelling correction in academic English translations using LSTM neural networks integrated with semantic analysis.
• Developed an LSTM neural network model incorporating semantic analysis for spelling correction.
• Tested the model on various academic English translation datasets to assess its effectiveness.
• Demonstrated significant improvements in spelling accuracy compared to traditional methods.
• Achieved a reduction of spelling errors by over 30% in academic translations.