Abstract
Accurate identification and analysis of semantics is beneficial for processing English texts effectively. This article briefly introduced Word2vec, which was used to extract semantic feature vectors from English texts, and the long short-term memory (LSTM) algorithm, which was used for semantic recognition of English texts. The relevant English comment texts were crawled from the Amazon movie database and used in a simulation experiment. The simulation experiment compared three algorithms: back propagation, recurrent neural network (RNN), and LSTM. The results showed that the LSTM algorithm’s recognition results for the part-of-speech and sentiment inclination of English texts were consistent with the label results. As the length of the English text increased, the recognition accuracy of all three algorithms decreased, and the LSTM algorithm had the smallest decrease. For the same length of English text, the LSTM algorithm had the highest accuracy in identifying part-of-speech and sentiment inclination, followed by the RNN algorithm, and the BP algorithm had the lowest. In terms of recognition time, the LSTM algorithm was the least.
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REFERENCES
Maiti, S., Garain, U., Dhar, A., and De, S., A novel method for performance evaluation of text chunking, Lang. Resour. Eval., 2015, vol. 49, no. 1, pp. 215–226. https://doi.org/10.1007/s10579-013-9250-3
Dhara, A.F. and Hendrawan, R., Rancang bangun ekstraksi ekspresi kata kerja pada ulasan pelanggan dengan text chunking untuk memaparkan pengalaman penggunaan produk, Jurnal Teknik ITS, 2017, vol. 6, no. 2. https://doi.org/10.12962/j23373539.v6i2.23151
Luo, J. and Lepage, Yv., Extraction of potentially useful phrase pairs for statistical machine translation, J. Inf. Process., 2015, vol. 23, no. 3, pp. 344–352. https://doi.org/10.2197/ipsjjip.23.344
Liu, M., Zhang, L., Hu, H., Nie, L., and Dai, J., A classification model for semantic entailment recognition with feature combination, Neurocomputing, 2005, vol. 208, pp. 127–135. https://doi.org/10.1016/j.neucom.2016.01.096
Xiong, Q., Research on English spoken semantic recognition machine learning model based on neural network and statistics fusion, J. Intell. Fuzzy Syst., 2020, vol. 38, no. 6, pp. 7341–7350. https://doi.org/10.3233/jifs-179808
Christie, G., Laddha, A., Agrawal, A., Antol, S., Goyal, Ya., Kochersberger, K., and Batra, D., Resolving vision and language ambiguities together: Joint segmentation & prepositional attachment resolution in captioned scenes, Comput. Vision Image Understanding, 2017, vol. 163, pp. 101–112. https://doi.org/10.1016/j.cviu.2017.09.001
Gu, B., Quan, X., Gu, Yu., Sheng, V.S., and Zheng, G., Chunk incremental learning for cost-sensitive hinge loss support vector machine, Pattern Recognit., 2018, vol. 83, pp. 196–208. https://doi.org/10.1016/j.patcog.2018.05.023
Li, B., Yang, X., Zhou, R., Wang, B., Liu, C., and Zhang, Ya., An efficient method for high quality and cohesive topical phrase mining, IEEE Trans. Knowl. Data Eng., 2019, vol. 31, no. 1, pp. 120–137. https://doi.org/10.1109/tkde.2018.2823758
Tang, D., Qin, B., Wei, F., Dong, K., Liu, T., and Zhou, M., A joint segmentation and classification framework for sentence level sentiment classification, IEEE/ACM Trans. Audio, Speech, Lang. Process., 2015, vol. 23, no. 11, pp. 1750–1761. https://doi.org/10.1109/TASLP.2015.2449071
Gao, X. and Fu, L., Methods of uncertain partial differential equation with application to internet public opinion problem, J. Intell. Fuzzy Syst., 2017, vol. 33, no. 3, pp. 1423–1433. https://doi.org/10.3233/jifs-17409
Wang, A., Liu, X., Sun, X., and Wang, J., Research of internet public opinion based on hybrid algorithm of LDA and VSM, C e Ca, 2017, vol. 42, no. 4, pp. 1508–1513.
Peng, Z., The approaches of internet public opinion research, Libr. J., 2016, vol. 35, no. 12, pp. 63–68. https://doi.org/10.1163/2213-6002_crpn_li_9789004279957_0012
Germann, U., Sampling phrase tables for the moses statistical machine translation system, Prague Bull. Math. Linguist., 2015, vol. 104, no. 1, pp. 39–50. https://doi.org/10.1515/pralin-2015-0012
Jean, S., Firat, O., Cho, K., Memisevic, R., and Bengio, Yo., Montreal neural machine translation systems for WMT’15, Proc. Tenth Workshop on Statistical Machine Translation, Lisbon, 2015, Bojar, O., Chatterjee, R., Federmann, Ch., Haddow, B., Hokamp, Ch., Huck, M., Logacheva, V., and Pecina, P., Eds., Association for Computational Linguistics, 2015, pp. 134–140. https://doi.org/10.18653/v1/w15-3014
Zoph, B., Yuret, D., May, J., and Knight, K., Transfer learning for low-resource neural machine translation, Proc. 2016 Conf. on Empirical Methods in Natural Language Processing, Austin, Texas, 2016, Su, J., Duh, K., and Carreras, X., Eds., Association for Computational Linguistics, 2016, pp. 1568–1575. https://doi.org/10.18653/v1/d16-1163
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Shuangshuang Yu Extraction and Analysis of Semantic Features of English Texts under Intelligent Algorithms. Aut. Control Comp. Sci. 58, 109–115 (2024). https://doi.org/10.3103/S0146411624010115
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DOI: https://doi.org/10.3103/S0146411624010115