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.

A key element of deep convolutional neural networks is the task of predicting its input features. However, most existing approaches to classification tend to predict features that correspond to the input features. In this paper, we propose a novel deep recurrent neural network (RNN) architecture for classification tasks. Different from the conventional recurrent neural network, RNNs also use a layer-by-layer architecture designed for the task-dependent features. This is designed to handle a large number of features and a large number of input features. To this end, the RNN model contains two layers: a recurrent layer that contains a feature generator and a visual layer that contains visual features. Finally, visual features are extracted from the visual feature generator and visual features from the visual feature generator by exploiting the similarity within visual feature representation. We demonstrate the efficiency of our RNN architecture and demonstrate that the visual feature generator is able to predict the inputs well. This is achieved by incorporating spatial domain knowledge and deep recurrent neural networks and we show that the network is able to produce a more accurate classification score.

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

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    A Unified Framework for Fine-Grained Core Representation Estimation and ClassificationA key element of deep convolutional neural networks is the task of predicting its input features. However, most existing approaches to classification tend to predict features that correspond to the input features. In this paper, we propose a novel deep recurrent neural network (RNN) architecture for classification tasks. Different from the conventional recurrent neural network, RNNs also use a layer-by-layer architecture designed for the task-dependent features. This is designed to handle a large number of features and a large number of input features. To this end, the RNN model contains two layers: a recurrent layer that contains a feature generator and a visual layer that contains visual features. Finally, visual features are extracted from the visual feature generator and visual features from the visual feature generator by exploiting the similarity within visual feature representation. We demonstrate the efficiency of our RNN architecture and demonstrate that the visual feature generator is able to predict the inputs well. This is achieved by incorporating spatial domain knowledge and deep recurrent neural networks and we show that the network is able to produce a more accurate classification score.


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