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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Sense understanding of text conversation using temporal convolution neural network.

Sandeep Rathor1, Sanket Agrawal2

  • 1Department of CEA, GLA University, Mathura, India.

Multimedia Tools and Applications
|February 23, 2022
PubMed
Summary

This study introduces a novel Spatio-Temporal model using CNN and LSTM for real-time text conversation sense understanding, achieving high accuracy in sentence classification and sentiment analysis.

Keywords:
Advance machine learning techniqueSense understandingSpatio temporalTemporal CNNText processing

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Accurate understanding of text conversation sense is crucial for various applications.
  • Existing models often struggle with real-time processing and efficiency.

Purpose of the Study:

  • To propose a novel Spatio-Temporal model for real-time sense understanding in text conversations.
  • To classify sentences into eight distinct senses using the proposed model.
  • To evaluate the model's efficiency and capabilities on sentiment classification tasks.

Main Methods:

  • Development of a Spatio-Temporal cell by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM).
  • Application of the proposed model for sentence sense classification into eight categories.
  • Testing the model's performance on the IMDB sentiment classification dataset.

Main Results:

  • Achieved an F-Score of approximately 0.984 for sentence sense classification.
  • Obtained an accuracy of 89.27% on the IMDB sentiment classification dataset.
  • Demonstrated superior performance over CNN, Bi-LSTM, and CNN & LSTM combination models in terms of parameters and execution time.

Conclusions:

  • The proposed Spatio-Temporal model effectively understands text conversation sense in real-time.
  • The model exhibits high accuracy and efficiency in both sense and sentiment classification tasks.
  • This approach offers a promising advancement for natural language understanding applications.