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Word embedding empowered topic recognition in news articles.

Sidrah Kaleem1, Zakia Jalil2, Muhammad Nasir3

  • 1Department of Computer Science, International Islamic University, Islamabad, Islamabad, Islamabad, Pakistan.

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Summary
This summary is machine-generated.

This study introduces word embedding latent Dirichlet allocation (WELDA), a novel method for news topic recognition. WELDA improves text representation by fusing topic and word embedding models, achieving high accuracy on benchmark datasets.

Keywords:
Artificial intelligenceComputer visionNatural language processingNeural networksTopic modelingWord embedding

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Information Retrieval

Background:

  • The proliferation of digital news necessitates advanced methods for analyzing large volumes of text data.
  • Current news topic recognition approaches often lack standardized integration of topic and word embedding models.
  • Existing algorithms can be overly complex and fail to fully leverage the benefits of model fusion.

Discussion:

  • This research proposes a novel framework, word embedding latent Dirichlet allocation (WELDA), to address limitations in news topic recognition.
  • WELDA integrates probabilistic topic modeling (Latent Dirichlet Allocation with Gibbs sampling) with semantic insights from Word2Vec embeddings.
  • The framework extracts comprehensive text representations by combining topic and word embedding models, enhancing downstream natural language processing tasks.

Key Insights:

  • The proposed WELDA framework achieves superior performance in news topic recognition.
  • It demonstrates enhanced expressiveness and efficient dimensionality reduction by integrating document-topic relationships and contextual information.
  • WELDA significantly outperforms existing approaches, reaching 88% accuracy on 20NewsGroup and 97% on BBC News.

Outlook:

  • This integrated approach offers a standardized and effective solution for news text topic recognition.
  • Further research can explore variations in embedding techniques and classifier integration for even greater accuracy.
  • The framework has potential applications in content recommendation, trend analysis, and news summarization.