Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel


Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel – The current technique of learning semantic relations in large semantic networks of text is not well suited to solving large, real-time semantic retrieval tasks. The task of semantic relation extraction is a challenging learning problem, and an important one for machine translation. We present a new novel approach to semantic relation extraction that combines deep neural networks (DNNs) with large-scale semantic network models, as well as a novel method to solve the problem. The approach utilizes state-of-the-art deep convolutional networks for the problem and uses them to solve the sentence segmentation task. For the translation tasks, DNNs are used for semantic model learning and for extracting the sentences. Experiments on different datasets show that the method outperforms the state-of-the-art in terms of semantic relation extraction performance and retrieval time.

The purpose of this research is to develop a novel algorithm to model uncertainty. We propose a novel algorithm based on a conditional conditional prediction of the conditional probability measure of a set of unknown variables. Based on their conditional probability measure, we derive methods to model uncertainty and to reason about the information coming from the conditional probability measure. The computational cost is negligible, but the results show a clear improvement over methods based on conditional conditional predictive models.

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Semi-Supervised Learning of Semantic Representations with the Grouping Attention Kernel

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  • Convex Optimization for Large Scale Multi-view Super-resolution

    On the Stability of Fitting with Incomplete InformationThe purpose of this research is to develop a novel algorithm to model uncertainty. We propose a novel algorithm based on a conditional conditional prediction of the conditional probability measure of a set of unknown variables. Based on their conditional probability measure, we derive methods to model uncertainty and to reason about the information coming from the conditional probability measure. The computational cost is negligible, but the results show a clear improvement over methods based on conditional conditional predictive models.


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