Language sense classification model based on neural network

Research Article
Open access

Language sense classification model based on neural network

Letao Gu 1* , Yuxiang Wang 2 , Yihan Wu 3
  • 1 Guilin University of Electronic Technology    
  • 2 Shanghai Jian Qiao University    
  • 3 Henan University Minsheng College    
  • *corresponding author 2001630407@mails.guet.edu.cn
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230297
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

The common international language, English, is playing an increasingly important role in various fields with the rapid development of artificial intelligence in recent years. Artificial intelligence can improve students' English abilities as an additional teaching tool. Therefore, this study seeks the English language sense between different types of sentences based on Long Short-Term Memory (LSTM) and BERT model analysis sentences and generates a model to distinguish the types. This paper adapts the LSTM model and BERT model: first, this paper crawls the sentences from British Broadcasting Corporation (BBC) documentaries, podcasts, and YouTube and then constructs a data filter to remove the sentences with low quality and short. This paper analyzes the data set through the BERT module and LSTM model. this paper then compares the differences between different sentences in a large-scale corpus to generate a language model without long-term dependence. A model is expected to be generated after corpus analysis, and the model can be used to analyze new input statements and give their types. This study can help English learners improve their sense of the English language and the types of sentences they need to say in the face of different situations.

Keywords:

natural language process, LSTM, BERT, classification, language sense

Gu,L.;Wang,Y.;Wu,Y. (2023). Language sense classification model based on neural network. Applied and Computational Engineering,27,141-147.
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References

[1]. Decuypere, M., Alirezabeigi, S., Grimaldi, E., Hartong, S., Kiesewetter, S., Landri, P., ... & Broeck, P. V. (2023). Laws of Edu-Automation? Three Different Approaches to Deal with Processes of Automation and Artificial Intelligence in the Field of Education. Postdigital Science and Education, 5(1), 44-55.

[2]. Peters, M. A. (2018). Deep learning, education, and the final stage of automation. Educational Philosophy and Theory, 50(6-7), 549-553.

[3]. Deng, X., & Yu, Z. (2023). A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education. Sustainability, 15(4), 2940.

[4]. Solanki, R. K., Rajawat, A. S., Gadekar, A. R., & Patil, M. E. (2023). Building a Conversational Chatbot Using Machine Learning: Towards a More Intelligent Healthcare Application. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines (pp. 285-309). IGI Global.

[5]. Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35.

[6]. Choi, H., Kim, J., Joe, S., & Gwon, Y. (2021, January). Evaluation of BERT and alBERT sentence embedding performance on downstream NLP tasks. In 2020 25th International conference on pattern recognition (ICPR) (pp. 5482-5487). IEEE.

[7]. Masala, M., Ruseti, S., & Dascalu, M. (2020, December). RoBERT – A Romanian BERT Model. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 6626-6637).

[8]. Yao, L., & Guan, Y. (2018, December). An improved LSTM structure for natural language processing. In 2018 IEEE International Conference of Safety Produce Informatization (IICSPI) (pp. 565-569). IEEE.

[9]. Rahali, A., & Akhloufi, M. A. (2023). End-to-End Transformer-Based Models in Textual-Based NLP. AI, 4(1), 54-110.

[10]. Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labeling with recurrent neural networks, 37-45.

Cite this article

Gu,L.;Wang,Y.;Wu,Y. (2023). Language sense classification model based on neural network. Applied and Computational Engineering,27,141-147.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Decuypere, M., Alirezabeigi, S., Grimaldi, E., Hartong, S., Kiesewetter, S., Landri, P., ... & Broeck, P. V. (2023). Laws of Edu-Automation? Three Different Approaches to Deal with Processes of Automation and Artificial Intelligence in the Field of Education. Postdigital Science and Education, 5(1), 44-55.

[2]. Peters, M. A. (2018). Deep learning, education, and the final stage of automation. Educational Philosophy and Theory, 50(6-7), 549-553.

[3]. Deng, X., & Yu, Z. (2023). A Meta-Analysis and Systematic Review of the Effect of Chatbot Technology Use in Sustainable Education. Sustainability, 15(4), 2940.

[4]. Solanki, R. K., Rajawat, A. S., Gadekar, A. R., & Patil, M. E. (2023). Building a Conversational Chatbot Using Machine Learning: Towards a More Intelligent Healthcare Application. In Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines (pp. 285-309). IGI Global.

[5]. Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35.

[6]. Choi, H., Kim, J., Joe, S., & Gwon, Y. (2021, January). Evaluation of BERT and alBERT sentence embedding performance on downstream NLP tasks. In 2020 25th International conference on pattern recognition (ICPR) (pp. 5482-5487). IEEE.

[7]. Masala, M., Ruseti, S., & Dascalu, M. (2020, December). RoBERT – A Romanian BERT Model. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 6626-6637).

[8]. Yao, L., & Guan, Y. (2018, December). An improved LSTM structure for natural language processing. In 2018 IEEE International Conference of Safety Produce Informatization (IICSPI) (pp. 565-569). IEEE.

[9]. Rahali, A., & Akhloufi, M. A. (2023). End-to-End Transformer-Based Models in Textual-Based NLP. AI, 4(1), 54-110.

[10]. Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labeling with recurrent neural networks, 37-45.

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