Big Data Mining Analysis Technology for Natural Language Processing Robot Design

Yongqiang  Wang; Li  Yang; Zhixin  Lun

doi:10.6180/jase.202412_27(12).0008

Big Data Mining Analysis Technology for Natural Language Processing Robot Design

Computer Science and Information Engineering

Yongqiang Wang¹This email address is being protected from spambots. You need JavaScript enabled to view it., Li Yang¹, and Zhixin Lun²

¹College of Artificial Intelligence, Tangshan University, Tangshan, 063000, China

²Computing Center, Tangshan University, Tangshan, 063000, China

Received: June 20, 2023
Accepted: November 2, 2023
Publication Date: March 1, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202412_27(12).0008

In the context of information big data, breakthroughs have been made in artificial intelligence, natural machine language and other technologies, and robot interactive dialogue has become a reality. Seq2seq is a common natural machine language processing technology, but the traditional Seq2seq dialogue model faces the problem of lack of semantic information and long-distance dependence. Therefore, the traditional Seq2seq technology is studied, and BiLSTM and attention mechanism are used to optimize the Seq2seq dialogue model. The simulation experiment test shows that in the iterative loss performance test of the dialogue model with an increased attention mechanism, the overall curve of the BiLSTM model has a gentle trend, and the loss is lower than that of the LSTM model. At 100 iterations, the loss value of the LSTM model is 0.36 and the loss value of the BiLSTM model is 0.17 . In the music scene dialogue test, the LSTM model could not accurately understand the meaning of the dialogue, and the satisfaction rate was 45%. The BiLSTM model accurately recognized the meaning of the dialogue and responded correctly, with a satisfaction rating of 69%. The innovation of research content adopts BiLSTM and attention mechanism to optimize the traditional Seq2seq dialogue model, improve the language analysis ability of robots, and provide important reference significance for the development of robot intelligence.

Keywords: Data mining; Machine language; BiLSTM model; Seq2seq model

[1] A. Russo, G. D’Onofrio, A. Gangemi, F. Giuliani, M. Mongiovi, F. Ricciardi, F. Greco, F. Cavallo, P. Dario, D. Sancarlo, et al., (2019) “Dialogue systems and conversational agents for patients with dementia: The human– robot interaction" Rejuvenation research 22(2): 109–120. DOI: https: //doi.org/10.1089/rej.2018.2075.
[2] J. Hedges and M. Sadrzadeh, (2019) “A generalised quantifier theory of natural language in categorical compositional distributional semantics with bialgebras" Mathematical Structures in Computer Science 29(6): 783–809. DOI: https: //doi.org/10.1017/S0960129518000324.
[3] S. K. Parr and G. T. Gobbel, (2020) “Considerations for advancing nephrology research and practice through natural language processing" Kidney International 97(2): 263–265. DOI: https: //doi.org/10.1016/j.kint.2019.12.001.
[4] P. Li and K. Mao, (2019) “Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts" Expert Systems with Applications 115: 512–523. DOI: https: //doi.org/10.1016/j.eswa.2018.08.009.
[5] P. Li, H. Yu, W. Zhang, G. Xu, and X. Sun, (2020) “Sanli: A supervised attention based framework for natural language inference" Neurocomputing 407: 72–82. DOI: https: //doi.org/10.1016/j.neucom.2020.03.092.
[6] V. Sorin, Y. Barash, E. Konen, and E. Klang, (2020) “Deep learning for natural language processing in radiology—fundamentals and a systematic review" Journal of the American College of Radiology 17(5): 639–648. DOI: https: //doi.org/10.1016/j.jacr.2019.12.026.
[7] E. Ötle¸s, D. E. Kendrick, Q. P. Solano, M. Schuller, S. L. Ahle, M. H. Eskender, E. Carnes, and B. C. George, (2021) “Using natural language processing to automatically assess feedback quality: Findings from 3 surgical residencies" Academic Medicine 96(10): 1457–1460. DOI: https: //doi.org/10.1097/ACM.0000000000004153.
[8] D. Zhu, (2019) “Humor robot and humor generation method based on big data search through IOT" Cluster Computing 22: 9169–9175. DOI: https: //doi.org/10.1007/s10586-018-2097-z.
[9] T. Sono, S. Satake, T. Kanda, and M. Imai, (2019) “Walking partner robot chatting about scenery" Advanced Robotics 33(15-16): 742–755. DOI: https: //doi.org/10.1080/01691864.2019.1610062.
[10] S. Heath, J. Liddle, and J. Wiles, (2020) “The challenges of designing a robot for a satisfaction survey: Surveying humans using a social robot" International Journal of Social Robotics 12(2): 519–533. DOI: https://doi.org/10.1007/s12369-019-00604-0.
[11] E. K. Chiou, M. Demir, V. Buchanan, C. C. Corral, M. R. Endsley, G. J. Lematta, N. J. Cooke, and N. J. McNeese, (2021) “Towards human–robot teaming: Tradeoffs of explanation-based communication strategies in a virtual search and rescue task" International Journal of Social Robotics: 1–20. DOI: https: //doi.org/10.1007/s12369-021-00834-1.
[12] M. Shiomi, A. Nakata, M. Kanbara, and N. Hagita, (2021) “Robot reciprocation of hugs increases both interacting times and self-disclosures" International Journal of Social Robotics 13: 353–361. DOI: https: //doi.org/10.1007/s12369-020-00644-x.
[13] G. Chen, W. Sheng, Y. Li, Y. Ou, and Y. Gu, (2022) “Humanoid Robot Portrait Drawing Based on Deep Learning Techniques and Efficient Path Planning" Arabian Journal for Science and Engineering 47(8): 9459–9470. DOI: https: //doi.org/10.1007/s13369-021-06245-8.
[14] K. E. Gunther, A. L. Hild, and S. L. Bieber, (2018) “Natural Resource Experience Affects Engagement with Emotionally Primed Presentations of Science" Rangeland Ecology & Management 71(2): 163–170. DOI: https: //doi.org/10.1016/j.rama.2017.12.004.
[15] A. V. Karhade, M. E. Bongers, O. Q. Groot, E. R. Kazarian, T. D. Cha, H. A. Fogel, S. H. Hershman, D. G. Tobert, A. J. Schoenfeld, C. M. Bono, et al., (2020) “Natural language processing for automated detection of incidental durotomy" The Spine Journal 20(5): 695–700. DOI: https://doi.org/10.1016/j.spinee.2019. 12.006.
[16] J. Vermassen, K. Colpaert, L. De Bus, P. Depuydt, and J. Decruyenaere, (2020) “Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes" Journal of Critical Care 56: 203–207. DOI: https://doi.org/10.1016/j.jcrc.2020.01.007.
[17] N. Maganti, H. Tan, L. M. Niziol, S. Amin, A. Hou, K. Singh, D. Ballouz, and M. A. Woodward, (2019) “Natural language processing to quantify microbial keratitis measurements" Ophthalmology 126(12): 1722–1724. DOI: https: //doi.org/0.1016/j.ophtha.2019.06.003.
[18] S. K. Kang, K. Garry, R. Chung, W. H. Moore, E. Iturrate, J. L. Swartz, D. C. Kim, L. I. Horwitz, and S. Blecker, (2019) “Natural language processing for identification of incidental pulmonary nodules in radiology reports" Journal of the American College of Radiology 16(11): 1587–1594. DOI: https: //doi.org/10.1016/j.jacr.2019.04.026.
[19] S. Mor and G. Gupta, (2021) “Artificial intelligence and technical efficiency: The case of Indian commercial banks" Strategic Change 30(3): 235–245. DOI: https: //doi.org/10.1002/jsc.2406.
[20] N. Haristiani, A. Danuwijaya, M. Rifai, and H. Sarila, (2019) “Gengobot: A chatbot-based grammar application on mobile instant messaging as language learning medium" Journal of Engineering Science and Technology 14(6): 3158–3173.
[21] M. S. Baig, A. Imran, A. U. Yasin, A. H. Butt, and M. I. Khan, (2022) “Natural language to sql queries: A review" International Journal of Innovations in Science Technology 4: 147–162.
[22] R. L. Boyd and H. A. Schwartz, (2021) “Natural language analysis and the psychology of verbal behavior: The past, present, and future states of the field" Journal of Language and Social Psychology 40(1): 21–41. DOI: https: //doi.org/10.1177/0261927X20967028.
[23] J. Roh, S. Park, B.-K. Kim, S.-H. Oh, and S.-Y. Lee, (2021) “Unsupervised multi-sense language models for natural language processing tasks" Neural Networks 142: 397–409. DOI: https://doi.org/10.1016/j.neunet.2021.05.023.
[24] V. D. Badal, S. A. Graham, C. A. Depp, K. Shinkawa, Y. Yamada, L. A. Palinkas, H.-C. Kim, D. V. Jeste, and E. E. Lee, (2021) “Prediction of loneliness in older adults using natural language processing: exploring sex differences in speech" The American Journal of Geriatric Psychiatry 29(8): 853–866. DOI: https: //doi.org/10.1016/j.jagp.2020.09.009.
[25] V. D. Badal, S. A. Graham, C. A. Depp, K. Shinkawa, Y. Yamada, L. A. Palinkas, H.-C. Kim, D. V. Jeste, and E. E. Lee, (2021) “Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech" The American Journal of Geriatric Psychiatry 29(8): 853–866. DOI: https: //doi.org/10.1016/j.jagp.2020.09.009.