Papers with probabilistic modelling

    1 papers
    Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)

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    Challenge: Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean.
    Approach: They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels.
    Outcome: The proposed framework improves the current art in uncovering the ground-truth relation labels.

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