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Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Author

Listed:
  • Guang Li
  • Fangfang Liu
  • Ashutosh Sharma
  • Osamah Ibrahim Khalaf
  • Youseef Alotaibi
  • Abdulmajeed Alsufyani
  • Saleh Alghamdi

Abstract

Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

Suggested Citation

  • Guang Li & Fangfang Liu & Ashutosh Sharma & Osamah Ibrahim Khalaf & Youseef Alotaibi & Abdulmajeed Alsufyani & Saleh Alghamdi, 2021. "Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, May.
  • Handle: RePEc:hin:jnlmpe:9982305
    DOI: 10.1155/2021/9982305
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    Cited by:

    1. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    2. Vinay Gautam & Naresh K. Trivedi & Aman Singh & Heba G. Mohamed & Irene Delgado Noya & Preet Kaur & Nitin Goyal, 2022. "A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    3. Nishant Jha & Deepak Prashar & Osamah Ibrahim Khalaf & Youseef Alotaibi & Abdulmajeed Alsufyani & Saleh Alghamdi, 2021. "Blockchain Based Crop Insurance: A Decentralized Insurance System for Modernization of Indian Farmers," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    4. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    5. Shubham Joshi & T.P Anithaashri & Ravi Rastogi & Gaurav Choudhary & Nicola Dragoni, 2022. "IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks," Energies, MDPI, vol. 16(1), pages 1-13, December.

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