Multimodal corpus-driven English semantic association modeling with lightweight AI training optimization

This study constructs an English semantic association model based on multimodal corpus, aiming to improve semantic understanding accuracy and training efficiency. Adopting cross modal attention mechanism, multi task learning, and lightweight model architecture strategy to achieve semantic alignment, sample balance, and resource optimization. Experimental results have shown that the model can improve the matching accuracy by 12.1%, increase the F1 value of low-frequency categories by 0.12, and reduce inference latency by 39.8%. The ablation experiment validates the independent effectiveness of each strategy, and SOTA comparison shows that this model outperforms CLIP in both accuracy and resource consumption. This study not only expands the theoretical research of multimodal semantic modeling, but also provides a feasible solution with low resources and high performance for intelligent education and language understanding systems.