Semantic Analysis and Translation Optimization of English Sentences Based on Long Short-Term Memory (LSTM) Networks
TL;DR
A GAT-BiLSTM fusion model that integrates dependency syntactic analysis with graph attention mechanisms and bidirectional long short-term memory networks improves translation fidelity and semantic consistency while exhibiting strong robustness for structurally complex inputs.
Abstract
Accurate semantic analysis and translation of complex English sentences are essential for intelligent information interaction and multilingual communication in modern digital systems, including semantic communication frameworks and electromagnetic-enabled intelligent networks. To address semantic omissions and logical inconsistencies caused by long-distance dependencies and referential ambiguity, this study proposes a GAT-BiLSTM fusion model that integrates dependency syntactic analysis with graph attention mechanisms and bidirectional long short-term memory networks. A lightweight semantic graph is first constructed to capture structural dependencies, after which graph representations are adaptively fused with contextual features through a gating mechanism to obtain unified semantic embeddings. During decoding, semantic gating and multi-head attention collaboratively enhance contextual coherence and semantic alignment. Experimental results demonstrate that the proposed model achieves a BLEU score exceeding 68.7, subject and action semantic matching scores of 0.88 and 0.84, respectively, and a syntactic structure retention rate of 72% for complex sentences. The proposed framework effectively improves translation fidelity and semantic consistency while exhibiting strong robustness for structurally complex inputs. Furthermore, the semantic modeling strategy provides methodological support for multilingual information processing, semantic communication, and intelligent human– machine interaction in electromagnetic wave propagation and wireless communication environments.