Fluency-based machine learning methods for the evaluation of legal texts


Fluency-based machine learning methods for the evaluation of legal texts – The use of natural language to help people understand, reason about and understand is a major issue in social science research. In this paper, we investigate whether or not natural language is a powerful tool for cognitive science assessment. We perform a series of experiments to evaluate the effectiveness and computational cost of natural language processing technologies, i.e. cognitive systems and cognitive processing systems. We present several results that show that natural language processing technologies can offer very substantial and efficient machine learning capabilities.

We present a new method for visual semantic segmentation of images at a low-dimensional plane with respect to non-convex parameters. Our method, named Deep Visual Semidefinite Network (DVS-NN), models the input vector as a non-convex function on the manifold, and extracts a non-convex function from the manifold to compute high-dimensional parametrized subgraphs for each quadrant. In this paper, an adversarial training is performed with a low-dimensional parametric model and the discriminative information is computed jointly from the manifold and the parametric model. The learned semantic segmentation aims at obtaining more precise parameters, and the discriminative information can be decoded for a lower-dimensional space. Experiments on three benchmark datasets demonstrate the potential of our method in terms of image segmentation performance on different semantic segmentations tasks.

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Fluency-based machine learning methods for the evaluation of legal texts

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  • A Deep Learning Approach to Extracting Plausible Explanations From Chinese Handwriting Texts

    Adversarial-inspired Semi-supervised Adversarial Image SegmentationWe present a new method for visual semantic segmentation of images at a low-dimensional plane with respect to non-convex parameters. Our method, named Deep Visual Semidefinite Network (DVS-NN), models the input vector as a non-convex function on the manifold, and extracts a non-convex function from the manifold to compute high-dimensional parametrized subgraphs for each quadrant. In this paper, an adversarial training is performed with a low-dimensional parametric model and the discriminative information is computed jointly from the manifold and the parametric model. The learned semantic segmentation aims at obtaining more precise parameters, and the discriminative information can be decoded for a lower-dimensional space. Experiments on three benchmark datasets demonstrate the potential of our method in terms of image segmentation performance on different semantic segmentations tasks.


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