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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM.

Shujing Zhang1

  • 1Faculty of International Studies, Henan Normal University, Xinxiang, Henan 453000, China.

Computational Intelligence and Neuroscience
|July 26, 2021
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Summary

This study introduces a novel Hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model for enhanced natural language processing. The proposed deep learning approach integrates text features and language knowledge, achieving a 93.0% accuracy rate.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant advancements in various AI tasks.
  • CNNs excel at automatic feature representation learning, leading to widespread use in image and natural language processing.
  • Existing CNN architectures are continuously being refined for improved performance in complex tasks.

Purpose of the Study:

  • To enhance natural language processing (NLP) by developing a novel deep learning model.
  • To improve the accuracy of text language processing through the fusion of text features and language knowledge.
  • To analyze and summarize current special model structures in deep learning for NLP.

Main Methods:

  • Analysis of typical Convolutional Neural Network (CNN) model structures, including depth and width optimization.
  • Investigation of attention mechanisms to further boost model performance.
  • Proposal of a Hybrid CNN and Long Short-Term Memory (LSTM) model integrating text features and language knowledge.
  • Parameter optimization for the proposed hybrid model.

Main Results:

  • The proposed Hybrid CNN-LSTM model demonstrates improved text language processing capabilities.
  • Experimental results show the model achieves an accuracy of 93.0% on benchmark datasets.
  • The developed model outperforms existing reference models in literature for NLP tasks.

Conclusions:

  • The integration of text features and language knowledge within a Hybrid CNN-LSTM framework significantly enhances NLP performance.
  • The proposed model represents a notable advancement in deep learning applications for natural language processing.
  • Further research into specialized deep learning architectures can lead to substantial improvements in AI-driven language understanding.