Abstract
Deep learning is a very common tool for natural language processing. In this document the use of neural networks is proposed to perform the syntactic analysis of sentences. We have experimented with three different models of artificial neural networks to identify the subject and the predicate of a sentence. A test has also been conducted to determine the type of words that make up the sentence. The obtained results are different for each network tested: the first one obtains inadequate results, the second one reaches 60% success in the detection of the subject and the predicate, and the third achieves 100% in the detection of the subject and the predicate and identifies the type of word in 92% of cases.
Supported by Information and System Technologies Department and Project TIN2015-64776-C3-3-R of the Spanish Ministry of Science and Innovation, co-funded by the European Regional Development Fund.
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References
Deng, Y., Bao, F., Kong, Y., Ren, Z., Dai, Q.: Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 653–664 (2017)
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)
Bhave, A., Sharma, M., Janghel, R.R.: Music generation using deep learning. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds.) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol. 898. Springer, Singapore (2019)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Levine, R.D.: Syntactic Analysis: an HPSG-Based Approach. Cambridge Textbooks in Linguistics. Cambridge University Press, Cambridge (2017)
Di, W., Bhardwaj, A., Wei, J.: Deep Learning Essentials: Your Hands-on Guide to the Fundamentals of Deep Learning and Neural Network Modeling. Packt Publishing, Birmingham (2018)
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Muñoz-Valero, D., Rodriguez-Benitez, L., Jimenez-Linares, L., Moreno-Garcia, J. (2022). Syntactic Analysis of Sentences Using Deep Neural Networks. In: Cornejo, M.E., Kóczy, L.T., Medina-Moreno, J., Moreno-García, J. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems 2. Studies in Computational Intelligence, vol 955. Springer, Cham. https://doi.org/10.1007/978-3-030-88817-6_5
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DOI: https://doi.org/10.1007/978-3-030-88817-6_5
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