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Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling.

Hafsa Ennajari, Nizar Bouguila, Jamal Bentahar

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    This study introduces a new Bayesian embedded spherical topic model (ESTM) for text analysis. ESTM improves topic interpretability and text representations by using knowledge graphs and word embeddings in curved space.

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    Area of Science:

    • Natural Language Processing
    • Machine Learning
    • Computational Linguistics

    Background:

    • Traditional topic models struggle with topic interpretability due to lack of semantic understanding.
    • Existing knowledge-enhanced models often use Euclidean space, leading to information loss.

    Purpose of the Study:

    • To propose a Bayesian embedded spherical topic model (ESTM) for enhanced text analysis.
    • To improve topic interpretability and text representation quality.

    Main Methods:

    • Combines knowledge graphs and word embeddings.
    • Utilizes a non-Euclidean hypersphere space for modeling.
    • Employs a Bayesian probabilistic framework.

    Main Results:

    • ESTM successfully uncovers interpretable topics.
    • Learned text representations are of high quality.
    • Demonstrates effectiveness across multiple NLP tasks and datasets.

    Conclusions:

    • The proposed ESTM offers superior topic interpretability compared to traditional methods.
    • ESTM provides discriminative text representations beneficial for NLP tasks.
    • Modeling in non-Euclidean space enhances topic modeling performance.