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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Long short-term memory (LSTM)-based news classification model.

Chen Liu1

  • 1Nanjing Forestry University, Nanjing, Jiangsu, China.

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|May 30, 2024
PubMed
Summary

Bidirectional Long Short-Term Memory (BiLSTM) deep learning models achieve high accuracy in Chinese news classification. BiLSTM networks outperform unidirectional LSTM models by effectively utilizing contextual information for improved text classification.

Area of Science:

  • Natural Language Processing
  • Deep Learning
  • Artificial Intelligence

Background:

  • Accurate Chinese news classification is crucial for information retrieval and analysis.
  • Traditional methods often struggle to capture the nuances of contextual information in Chinese text.
  • Deep learning models offer a promising avenue for enhancing text classification performance.

Purpose of the Study:

  • To evaluate the effectiveness of unidirectional and bidirectional Long Short-Term Memory (LSTM) networks for Chinese news classification.
  • To investigate the impact of contextual information on text classification accuracy.
  • To develop an optimized deep learning model for Chinese news categorization.

Main Methods:

  • Preprocessed Chinese text using jieba for word segmentation, stop-word removal, and word frequency analysis.

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  • Generated word vectors using word2vec for feature input into LSTM models.
  • Implemented bidirectional LSTM (BiLSTM) for forward and backward information transfer, followed by an LSTM for feature integration.
  • Utilized an adaptive moment estimation (Adam) optimizer and dropout layers for hyperparameter optimization and overfitting reduction.

    • Main Results:

    • The bidirectional LSTM (BiLSTM) model achieved an F1-score of 94.15% for Chinese news classification.
    • The unidirectional LSTM model obtained an F1-score of 93.16%.
    • BiLSTM demonstrated superior performance in feature extraction, effectively leveraging contextual information.

    Conclusions:

    • Bidirectional LSTM networks are highly effective for Chinese news classification, outperforming unidirectional LSTM models.
    • The proposed deep learning approach accurately classifies Chinese news articles by effectively utilizing contextual information.
    • This study highlights the significance of deep learning in advancing natural language processing tasks for the Chinese language.