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Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution.

Junbiao Pang, Anjing Hu, Qingming Huang

    IEEE Transactions on Cybernetics
    |July 12, 2018
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a novel two-step method for web topic detection, improving topic coherence and interpretability by identifying representative prototype webpages. The approach enhances accuracy in understanding trends from multimodal web data.

    Area of Science:

    • Computer Science
    • Data Mining
    • Web Analytics

    Background:

    • Organizing webpages into topics is crucial for understanding trends in multimodal web data.
    • User-generated web content is often sparse, noisy, and less constrained, leading to inefficient feature representations.
    • This inefficiency results in detected topics containing irrelevant webpages, reducing coherence, interpretability, and usefulness.

    Purpose of the Study:

    • To address the challenge of inaccurate web topic detection caused by noisy data.
    • To improve the coherence, interpretability, and usefulness of detected web topics.
    • To propose a novel two-step approach for web topic detection based on topic prototypes.

    Main Methods:

    • A sparse Poisson deconvolution method was developed to learn intratopic similarities between webpages.

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  • A submodularity function was utilized to identify top-k diverse and representative prototype webpages.
  • The approach follows a detection-by-ranking strategy.

    • Main Results:

    • Experimental results demonstrated improved accuracies in the web topic detection task.
    • The method enhanced the interpretability of topics by using identified prototypes.
    • The approach was validated on two public datasets.

    Conclusions:

    • The proposed two-step method effectively improves web topic detection accuracy.
    • Interpreting topics via prototypes enhances their coherence and usefulness.
    • This research offers a more robust approach to analyzing trends in multimodal web data.