Abstract
In this article, the authors consider the problem of text generation for low- resource languages, using the Kazakh language as an example, based on semantic analysis. Machine learning method is used in the generation of text documents and sources in the Kazakh language. First, semantic analysisis performed, the number of words in the given text, the number of stop words, the number of symbols, etc. Then the TF-IDF algorithm is used to find the semantically important words of the text. Annotation of the given text by means of semantic analysis. And at the end, generation of text with advanced semantic analysis. A corpus for the Kazakh language was prepared for experiments and research. GPT-3 and NLG are used in the process of generation. Generation by means of semantic analysis of the text gives us some great opportunities. The Recurrent Neural Network (RNN) method is used during generation.Generation gives us a lot of opportunities, including not spending time on unnecessary information. It will provide an article or short text related to the keywords you searched for. The description of the developed approach and practical results of experiments are presented.
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References
Yemm, G.: Can NLP help or harm your business? (2006)
Ranjan, S., Sood, S., Verma, V.: Twitter sentiment analysis of real-time customer experience feedback for predicting growth of Indian telecom companies. In: 2018 4th International Conference on Computing Sciences (ICCS). IEEE (2018)
Liu, B.: Sentiment analysis: a multi-faceted problem. IEEE Intell. Syst. 25(3), 76–80 (2017)
Yergesh, B., Bekmanova, G., Sharipbay, A.: Sentiment analysis on the hotel reviews in the Kazakh language. In: 2017 International Conference on Computer Science and Engineering (UBMK). IEEE (2017)
Phani, S., Lahiri, S., Biswas, A.: Sentiment analysis of tweets in three Indian languages. In: Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP 2016) (2016)
Baly, R., El-Khoury, G., Moukalled, R., Aoun, R., Hajj, H., Shaban, K.B., El-Hajj, W.: Comparative evaluation of sentiment analysis methods across Arabic dialects. Procedia Comput. Sci. 117, 266–273 (2017)
Yildirim, E., Çetin, F.S., Eryigit, G., Temel, T.: The impact of NLP on Turkish sentiment analysis (2016)
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. CST 463-Advanced Machine Learning. https://catalog.csumb.edu/preview_course_nopop.php?catoid=1&coid=476. Accessed 10 Nov 2022
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) Network. https://arxiv.org/abs/1808.03314. Accessed 28 Oct 2022
Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Kremer, S.C., Kolen, J.F. (eds.) A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press (2001)
Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. Inverse Probl. 34(1), 014004 (2017)
Rakhimova, D., Turarbek, A., Kopbosyn, L.: Hybrid approach for the semantic analysis of texts in the Kazakh language. In: Hong, T.-P., Wojtkiewicz, K., Chawuthai, R., Sitek, P. (eds.) ACIIDS 2021. CCIS, vol. 1371, pp. 134–145. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1685-3_12
Diana, R., Assem, S.: Problems of semantics of words of the Kazakh language in the information retrieval. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11684, pp. 70–81. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28374-2_7
Rakhimova, D., Turganbayeva, A.: Approach to extract keywords and keyphrases of text resources and documents in the Kazakh language. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds.) ICCCI 2020. LNCS (LNAI), vol. 12496, pp. 719–729. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63007-2_56
Rakhimova, D., Turganbayeva, A.: Auto-abstracting of texts in the Kazakh language. In: Proceedings of the 6th International Conference on Engineering & MIS, pp. 1–5 (2020)
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This research was performed and financed by the grant Project IRN AP 09259556 of Ministry of Science and Higher Education of the Republic of Kazakhstan.
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Rakhimova, D., Abilay, S., Kuralay, A. (2023). The Task of Generating Text Based on a Semantic Approach for a Low-Resource Kazakh Language. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_48
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DOI: https://doi.org/10.1007/978-3-031-42430-4_48
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