Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Traditional dictionaries and machine translation lack the nuance required for effective ESL writing.
• Existing AI models for writing assistance face challenges with data sparsity and interpretability.

Purpose of the Study:

• To develop and analyze AI-driven models for improving English writing.
• To enhance ESL vocabulary acquisition and grammatical error detection using neural networks.

Main Methods:

• Utilized unsupervised disambiguation and clustering of multimedia contexts based on word vectors.
• Developed a collocation intention description and retrieval method using dependency grammar and semantic metrics.
• Constructed and compared three algorithmic models: Conditional Random Field, LSTM-CRF, and a multitask learning model.

Main Results:

• Achieved over 0.7 disambiguation accuracy and covered 90% of vocabulary learning tasks with multimedia contexts.
• Multimedia context learning systems improved ESL vocabulary learning effectiveness and long-term memory by 30%.
• Collocation retrieval usage doubled, creating a "sticky" ESL writing aid; multitask learning model outperformed others in grammatical error detection.

Conclusions:

• AI, particularly neural networks, offers significant improvements for ESL writing tools.
• Multimedia context and advanced AI models address limitations in current vocabulary learning and grammar correction.
• The developed methods enhance user experience and learning outcomes for English as a Second Language (ESL) learners.