Semantic Analysis and Translation Optimization of English Sentences Based on Long Short-Term Memory (LSTM) Networks
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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.
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References
H. Jung, K. Kim, H. Shin J, H. Na S, S. Jung, and S. & Woo, “(2022),” Impact of sentence representation matching in neural machine translation. Applied Sciences, 12(3), pp. 1313-1331. https://doi.org/10.3390/app12031313.
X. Shi, H. Huang, P. Jian, and K. & Tang Y, “(2021),” Improving neural machine translation with sentence alignment learning. Neurocomputing, 420(1), pp. 15-26. https://doi.org/10.1016/j.neucom.2020.05.104.
S. Wang, H. Huang, and S. & Shi, “(2022),” Improving non-autoregressive machine translation using sentence-level semantic agreement. Applied Sciences, 12(10), pp. 5003-5015. https://doi.org/10.3390/app12105003.
L. Kang, S. He, M. Wang, F. Long, and J. & Su, “(2023),” Bilingual attention based neural machine translation. Applied Intelligence, 53(4), pp. 4295-4315. https://doi.org/10.1007/s10489-022-03563-8.
M. Mahsuli M, S. Khadivi, and M. & Homayounpour M, “(2023),” Lenm: improving low-resource neural machine translation using target length modeling. Neural processing letters, 55(7), pp. 9435-9466. https://doi.org/10.1007/s11063-023-11208-1.
X. Geng, L. Wang, X. Wang, M. Yang, X. Feng, B. Qin, and Z. & Tu, “(2022),” Learning to refine source representations for neural machine translation. International Journal of Machine Learning and Cybernetics, 13(8), pp. 2199-2212. https://doi.org/10.1007/s13042-022-01515-9.
P. Wang, “(2022),” A study of an intelligent algorithm combining semantic environments for the translation of complex English sentences. Journal of Intelligent Systems, 31(1), pp. 623-631. https://doi.org/10.1515/jisys-2022-0048.
S. Harada and T. & Watanabe, “(2022),” Neural machine translation with synchronous latent phrase structure. Journal of Natural Language Processing, 29(2), pp. 587-610. https://doi.org/10.5715/jnlp.29.587.
Y. Sang, Y. Chen, and J. & Zhang, “(2023),” Neural machine translation research on syntactic information fusion based on the field of electrical engineering. Applied Sciences, 13(23), pp. 12905-12919. https://doi.org/10.3390/app132312905.
G. Duan, H. Yang, K. Qin, and T. & Huang, “(2021),” Improving neural machine translation model with deep encoding information. Cognitive Computation, 13(4), pp. 972-980. https://doi.org/10.1007/s12559-021-09860-7.
C. ZHANG Y, X. GAO S, T. YU Z, H. WANG Z, and L. & MAO C, “(2023),” A Chinese-Vietnamese neural machine translation method using the dual representation of BERT and word embedding. Computer Engineering & Science, 45(03), pp. 546-554.
Z. Hlaing Z, K. Thu Y, T. Supnithi, and P. & Netisopakul, “(2022),” Improving neural machine translation with POS-tag features for low-resource language pairs. Heliyon, 8(8), pp. 1-22. https://doi.org/10.1016/j.heliyon.2022.e10375.
H. Choi G, H. Shin J, H. Lee Y, and K. & Kim Y, “(2022),” Toward Understanding Most of the Context in Document-Level Neural Machine Translation. Electronics, 11(15), pp. 2390-2408. https://doi.org/10.3390/electronics11152390.
J. Unanue I, Z. Borzeshi E, and M. & Piccardi, “(2022),” Regressing word and sentence embeddings for low-resource neural machine translation. IEEE Transactions on Artificial Intelligence, 4(3), pp. 450-463. https://doi.org/10.1109/TAI.2022.3187680.
A. Setyanto, A. Laksito, F. Alarfaj, M. Alreshoodi, I. Oyong, M. Hayaty, et al., “(2022),” Arabic language opinion mining based on long short-term memory (LSTM). Applied Sciences, 12(9), pp. 4140-4148. https://doi.org/10.3390/app12094140.
S. Chen, “(2024),” An Intelligent Algorithm for Semantic Feature Analysis and Translation of English Texts. Journal of ICT Standardization, 12(3), pp. 271-282. https://doi.org/10.13052/jicts2245-800X.1232.
H. He, “(2023),” An intelligent algorithm for fast machine translation of long English sentences. Journal of Intelligent Systems, 32(1), pp. 0257-20220266. https://doi.org/10.1515/jisys-2022-0257, 2022.
V. Preethi and U. & Kiruthika, “(2021),” Survey on text transformation using Bi-LSTM in natural language processing with text data. Turkish Journal of Computer and Mathematics Education, 12(9), pp. 2577-2585.
S. Karunarathna K M G and M. & Rupasingha R A H, “(2022),” Learning to use normalization techniques for preprocessing and classification of text documents. International Journal of Multi-disciplinary Studies, 9(2), pp. 69-81.
K. Markov A, O. Semenochkin D, G. Kravets A, and A. & Yanovskiy T, “(2024),” Comparative analysis of applied natural language processing technologies for improving the quality of digital document classification. International Journal of Open Information Technologies, 12(3), pp. 66-77.
B. Ren, “(2021),” The use of machine translation algorithm based on residual and LSTM neural network in translation teaching. Plos one, 15(11), pp. 0240663-0240679. https://doi.org/10.1371/journal.pone.0240663.
L. Zhao, W. Gao, and J. & Fang, “(2021),” High-performance english-chinese machine translation based on GPU-enabled deep neural networks with domain corpus. Applied Sciences, 11(22), pp. 10915-10932. https://doi.org/10.3390/app112210915.
M. Gupta and P. & Kumar, “(2021),” Robust neural language translation model formulation using Seq2seq approach. Fusion: Practice and Applications, 5(2), pp. 61-67. https://doi.org/10.54216/FPA.050203.
Q. Wang, “(2022),” Semantic analysis technology of English translation based on deep neural network. Computational Intelligence and Neuroscience, pp. (1), 1176943-1176952. https://doi.org/10.1155/2022/1176943, 2022.
J. Yu and X. & Ma, “(2022),” English translation model based on intelligent recognition and deep learning. Wireless Communications and Mobile Computing, pp. (1), 3079775-3079784. https://doi.org/10.1155/2022/3079775, 2022.
X. Guo, “(2022),” Optimization of English machine translation by deep neural network under artificial intelligence. Computational intelligence and neuroscience, pp. (1), 2003411-2003421. https://doi.org/10.1155/2022/2003411.
F. Li, J. Chen, and X. & Zhang, “(2023),” A survey of non-autoregressive neural machine translation. Electronics, 12(13), pp. 2980-3011. https://doi.org/10.3390/electronics12132980.
C. Wang, “(2021),” Efficient English translation method and analysis based on the hybrid neural network. Mobile Information Systems, pp. (1), 9985251-9985261. https://doi.org/10.1155/2021/9985251, 2021.
S. Lv, B. Yang, R. Wang, S. Lu, J. Tian, W. Zheng, and L. & Yin, “(2024),” Dynamic multi-granularity translation system: DAG-structured multi-granularity representation and self-attention. Systems, 12(10), pp. 420-435. https://doi.org/10.3390/systems12100420.
J. Yang, “(2023),” ECBTNet: English-Foreign Chinese intelligent translation via multi-subspace attention and hyperbolic tangent LSTM. Neural Computing and Applications, 35(36), pp. 25001-25011. https://doi.org/10.1007/s00521-023-08624-8.
L. Ding, L. Wang, and S. & Liu, “(2023),” Recurrent graph encoder for syntax-aware neural machine translation. International Journal of Machine Learning and Cybernetics, 14(4), pp. 1053-1062. https://doi.org/10.1007/s13042-022-01682-9.
R. Peng, T. Hao, and Y. & Fang, “(2021),” Syntax-aware neural machine translation directed by syntactic dependency degree. Neural Computing and Applications, 33(23), pp. 16609-16625. https://doi.org/10.1007/s00521-021-06256-4.
K. Chen, R. Wang, M. Utiyama, and E. & Sumita, “(2021),” Integrating prior translation knowledge into neural machine translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30(1), pp. 330-339. https://doi.org/10.1109/TASLP.2021.3138714.
L. He, Q. Meng, Q. Zhang, J. Duan, and H. & Wang, “(2023),” Event detection using a self-constructed dependency and graph convolution network. Applied Sciences, 13(6), pp. 3919-3929. https://doi.org/10.3390/app13063919.
F. Wan and P. & Li, “(2024),” A neural machine translation method based on split graph convolutional self-attention encoding. PeerJ Computer Science, 10(1), pp. 1886-1907. https://doi.org/10.7717/peerj-cs.1886.