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Correlated Topic Modeling for Short Texts in Spherical Embedding Spaces.

Hafsa Ennajari, Nizar Bouguila, Jamal Bentahar

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    We introduce a new Spherical Correlated Topic Model (SCTM) for analyzing short texts. This model improves topic coherence and document classification by integrating word and knowledge graph embeddings.

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    Area of Science:

    • Natural Language Processing
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Short text analysis is crucial due to the prevalence of data like headlines and tweets.
    • Modeling short texts is challenging due to their sparse and noisy characteristics.

    Purpose of the Study:

    • To propose a novel Spherical Correlated Topic Model (SCTM) for enhanced short text analysis.
    • To capture semantic relationships and topic correlations effectively in short texts.

    Main Methods:

    • Developed the Spherical Correlated Topic Model (SCTM).
    • Integrated word embeddings and knowledge graph embeddings.
    • Utilized the von Mises-Fisher distribution for modeling high-dimensional embeddings on a hypersphere.

    Main Results:

    • SCTM demonstrated superior performance in topic coherence and document classification compared to existing models.
    • The model effectively preserves angular relationships between topic vectors.
    • Incorporation of knowledge graph embeddings enriched semantic understanding.

    Conclusions:

    • The proposed SCTM offers an effective approach for short text analysis.
    • SCTM provides interpretable topics and reveals meaningful correlations.
    • This model advances the field of topic modeling for sparse textual data.