Skip to main content
SpringerLink
Log in

Construction of Internet of Things English terms model and analysis of language features via deep learning

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

This exploration aims to attain more detailed technical terms and form structured knowledge representation. An unsupervised knowledge representation learning method is constructed here, and a term model is constructed by the neural network of deep learning to drill down the English terms in the Internet of Things (IoT). After analyzing the IoT English language characteristics, an expert interaction method is proposed based on the Delphi method. Besides, the N-Gram model is utilized for unsupervised candidate acquisition process to break up long text into short fragments, and each fragment is a possible word string combination. The data used in this experiment are obtained from the Web of Science database, and two term lists including “adaptive control” and “self-learning” are selected for data retrieval. Meanwhile, the term frequency-inverse document frequency value is used to preliminarily screen the words. With the text in the IoT field as the experimental object, the influence of different N-Gram numbers on the system is analyzed from three aspects, namely system running time, average memory occupancy rate, and F1 value of term extraction. The experimental results demonstrate that when the number of N-Gram increases, the overall running time of the system increases, and the memory load also enlarges when performing the operation task. According to the F1 value of term extraction, when N = 1, 2, 3, and 4, the F1 value has reached the highest level. If the number of N-Grams continues to increase, the F1 value of term extraction by the system will decrease. When K is equal to 4 and 6, respectively, the Silhouette Coefficient results of the terms “adaptive control” and “self-learning” turn separately, so the two terms are classified into categories 4 and 6 categories accordingly. In summary, the deep learning technology can effectively and automatically extract professional terms from the original text, and classify the extraction results according to the original terminology database. Compared with existing methods, core experts play a central role in acquiring knowledge in specific areas, while external experts play an indispensable role in enriching and improving technology systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Gul S, Asif M, Ahmad S et al (2017) A survey on role of internet of things in education. Int J Comput Sci Netw Secur 17(5):159–165. https://doi.org/10.11591/ijeecs.v19.i1.pp215-221

    Article  Google Scholar 

  2. Al-Janabi S, Al-Shourbaji I, Shojafar M, et al (2017) Mobile cloud computing: challenges and future research directions. In: IEEE, 2017 10th International Conference on Developments in eSystems Engineering (DeSE), Paris pp 62–67. doi: https://doi.org/10.1109/DeSE.2017.21

  3. Mohammed M (2019) Pragmatic text mining method to find the topics of citation network. Big Data Netw Technol 81:190. https://doi.org/10.1007/978-3-030-23672-4_15

    Article  Google Scholar 

  4. Banerjee M, Lee J, Choo KKR (2018) A blockchain future for internet of things security: a position paper. Digit Commun Netw 4(3):149–160. https://doi.org/10.1016/j.dcan.2017.10.006

    Article  Google Scholar 

  5. Chen S (2021) Design of internet of things online oral English teaching platform based on long-term and short-term memory network. Int J Contin Eng Educ Life Long Learn 31(1):104–118. https://doi.org/10.1504/IJCEELL.2021.10032669

    Article  Google Scholar 

  6. Mohammad M (2019) Multi-level network construction based on intelligent big data analysis. Big Data Smart Digit Environ 53:102. https://doi.org/10.1007/978-3-030-12048-1_13

    Article  MathSciNet  Google Scholar 

  7. Joseph N, Kar AK, Ilavarasan PV et al (2017) Review of discussions on internet of things (IoT): insights from twitter analytics. J Glob Inf Manag (JGIM) 25(2):38–51. https://doi.org/10.4018/JGIM.2017040103

    Article  Google Scholar 

  8. Guo B, Zhang D, Wang Z et al (2013) Opportunistic IoT: exploring the harmonious interaction between human and the internet of things. J Netw Comput Appl 36(6):1531–1539. https://doi.org/10.1016/j.jnca.2012.12.028

    Article  Google Scholar 

  9. Jung GY, Yoon SS, Kang JY (2019) Development of text mining-based accounting terminology analyzer for financial information utilization. J Inf Syst 28(4):155–174. https://doi.org/10.29138/ijebd.v3i3.1131

    Article  Google Scholar 

  10. Heyman G, Vulić I, Moens MF (2018) A deep learning approach to bilingual lexicon induction in the biomedical domain. BMC Bioinform 19(1):1–15. https://doi.org/10.1186/s12859-018-2245-8

    Article  Google Scholar 

  11. Akhtar MS, Garg T, Ekbal A (2020) Multi-task learning for aspect term extraction and aspect sentiment classification. Neurocomputing 398:247–256. https://doi.org/10.1016/j.neucom.2020.02.093

    Article  Google Scholar 

  12. Shen C, Chen M, Wang C (2019) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Hum Behav 101:474–483. https://doi.org/10.1016/j.chb.2018.09.031

    Article  Google Scholar 

  13. Shen C, Luong T, Ho J et al (2020) Social media marketing of IT service companies: analysis using a concept-linking mining approach. Ind Mark Manag 90:593–604. https://doi.org/10.1016/j.indmarman.2019.11.014

    Article  Google Scholar 

  14. Shaikh FK, Zeadally S, Exposito E (2015) Enabling technologies for green internet of things. IEEE Syst J 11(2):983–994. https://doi.org/10.1109/JSYST.2015.2415194

    Article  Google Scholar 

  15. HaddadPajouh H, Khayami R, Dehghantanha A et al (2020) AI4SAFE-IoT: an AI-powered secure architecture for edge layer of internet of things. Neural Comput Appl 32(20):16119–16133. https://doi.org/10.1007/s00521-020-04772-3

    Article  Google Scholar 

  16. Bader SR, Maleshkova M, Lohmann S (2019) Structuring reference architectures for the industrial internet of things. Future Internet 11(7):151. https://doi.org/10.3390/fi11070151

    Article  Google Scholar 

  17. Din IU, Guizani M, Rodrigues JJPC et al (2019) Machine learning in the internet of things: designed techniques for smart cities. Futur Gener Comput Syst 100:826–843. https://doi.org/10.1016/j.future.2019.04.017

    Article  Google Scholar 

  18. Russo G, Marsigalia B, Evangelista F et al (2015) Exploring regulations and scope of the Internet of Things in contemporary companies: a first literature analysis. J Innov Entrep 4(1):1–13. https://doi.org/10.1186/s13731-015-0025-5

    Article  Google Scholar 

  19. Hajiheidari S, Wakil K, Badri M et al (2019) Intrusion detection systems in the Internet of things: a comprehensive investigation. Comput Netw 160:165–191. https://doi.org/10.1016/j.comnet.2019.05.014

    Article  Google Scholar 

  20. Fu H, Wang M, Li P et al (2019) Tracing knowledge development trajectories of the internet of things domain: a main path analysis. IEEE Trans Industr Inf 15(12):6531–6540. https://doi.org/10.1109/TII.2019.2929414

    Article  Google Scholar 

  21. Hohman F, Kahng M, Pienta R et al (2018) Visual analytics in deep learning: an interrogative survey for the next frontiers. IEEE Trans Visual Comput Gr 25(8):2674–2693. https://doi.org/10.1109/TVCG.2018.2843369

    Article  Google Scholar 

  22. Zhang S, Yao L, Sun A et al (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38. https://doi.org/10.1145/3285029

    Article  Google Scholar 

  23. Kooijman PC, Nagornov KO, Kozhinov AN et al (2019) Increased throughput and ultra-high mass resolution in DESI FT-ICR MS imaging through new-generation external data acquisition system and advanced data processing approaches. Sci Rep 9(1):1–11. https://doi.org/10.1038/s41598-018-36957-1

    Article  Google Scholar 

  24. Lamarche G, Lurton X (2018) Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars. Mar Geophys Res 39(1):5–22. https://doi.org/10.1007/s11001-017-9315-6

    Article  Google Scholar 

  25. Eisenstein F, Danev R, Pilhofer M (2019) Improved applicability and robustness of fast cryo-electron tomography data acquisition. J Struct Biol 208(2):107–114. https://doi.org/10.1016/j.jsb.2019.08.006

    Article  Google Scholar 

  26. Puce A, Hämäläinen MS (2017) A review of issues related to data acquisition and analysis in EEG/MEG studies. Brain Sci 7(6):58. https://doi.org/10.3390/brainsci7060058

    Article  Google Scholar 

  27. Zarif F, Kessouri P, Slater L (2017) Recommendations for field-scale induced polarization (IP) data acquisition and interpretation. J Environ Eng Geophys 22(4):395–410. https://doi.org/10.2113/JEEG22.4.395

    Article  Google Scholar 

  28. Radhakrishnan S, Erbis S, Isaacs JA et al (2017) Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE 12(3):e0172778. https://doi.org/10.1371/journal.pone.0172778

    Article  Google Scholar 

  29. White AE, Moeller J, Ivcevic Z et al (2018) Gender identity and sexual identity labels used by US high school students: a co-occurrence network analysis. Psychol Sex Orientat Gend Divers 5(2):243. https://doi.org/10.1037/sgd0000266

    Article  Google Scholar 

  30. Hirano H, Takemoto K (2019) Difficulty in inferring microbial community structure based on co-occurrence network approaches. BMC Bioinform 20(1):1–14. https://doi.org/10.1186/s12859-019-2915-1

    Article  Google Scholar 

  31. Al-Janabi S, Alkaim A, Al-Janabi E et al (2021) Intelligent forecaster of concentrations (PM2. 5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl. https://doi.org/10.1007/s00521-021-06067-7

    Article  Google Scholar 

  32. Peng H, Bao M, Li J et al (2018) Incremental term representation learning for social network analysis. Futur Gener Comput Syst 86:1503–1512. https://doi.org/10.1016/j.future.2017.05.020

    Article  Google Scholar 

  33. Motameni H (2020) Determining the composition functions of Persian non-standard sentences in terminology using a deep learning fuzzy neural network model. Int J Eng 33(12):2471–2481. https://doi.org/10.5829/ije.2020.33.12c.06

    Article  Google Scholar 

  34. Wang D, Su J, Yu H (2020) Feature extraction and analysis of natural language processing for deep learning English language. IEEE Access 8:46335–46345. https://doi.org/10.1109/ACCESS.2020.2974101

    Article  Google Scholar 

  35. Shen C-W, Min C, Wang C-C (2019) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Hum Behav 101:474–483. https://doi.org/10.1016/j.chb.2018.09.031

    Article  Google Scholar 

  36. Liu Y, Chen M (2021) Applying text similarity algorithm to analyze the triangular citation behavior of scientists. Appl Soft Comput 107:107362. https://doi.org/10.1016/j.asoc.2021.107362

    Article  Google Scholar 

Download references

Funding

This research was supported by following fundings: 1. The Integration of College English Teaching and Innovation and Entrepreneurship Education: Paths and Practice GSSKB20-23 Founded by Foreign Language Teaching Projects in Colleges and Universities of Gansu Province [2020–2022]. 2. Pedagogical Exploration on the Communication among China and the Countries along “Belt and Road” Founded by Academic Administration of Lanzhou Jiaotong University [2021–48].

Author information

Authors and Affiliations

  1. School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou, China

    Yongbin Li

Authors
  1. Yongbin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongbin Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y. Construction of Internet of Things English terms model and analysis of language features via deep learning. J Supercomput 78, 6296–6317 (2022). https://doi.org/10.1007/s11227-021-04130-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04130-7

Keywords

Search

Navigation