Automating and Explaining Polygraph-based Translation in Sub-Territorial Corpora Using Wikidata

Automating and Explaining Polygraph-based Translation in Sub-Territorial Corpora Using Wikidata – Convarting a given data into semantic sentences is a difficult task for the machine-learning community as it requires the human’s ability to understand a set of variables which must be interpreted to understand it. In this paper, a novel convolutional neural network (CNN) is proposed to facilitate the interpretation of a given sentence by means of a deep learning technique. The system, called Multi-task, is trained using a variety of data sets which have a range of semantic topics and a large number of sentences belonging to a given topic. After a series of experiments the results show that the proposed network can correctly classify data into both semantic and sentence parts of a given text and outperform state-of-art CNNs in terms of the number of semantic sentences and the accuracy of comprehension of the sentences. Further, the proposed model is particularly effective when using a large corpus to study complex sentence structures.

Sparse semantic segmentation from a dataset can be obtained from a text graph, by using a graph semantic graph (SVG). In this work, we present a new data visualization technique of the semantic graph as well as a simple feature extraction technique from graph graphs. In other words, the feature extraction method can be used to produce semantic segmentation results. The method is based on the idea of learning a graph representation of the semantic graph and learning a segmentation function to segment each node of the graph. Experimental results show that our algorithm can efficiently extract semantic segmentation results with very few parameters.

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Automating and Explaining Polygraph-based Translation in Sub-Territorial Corpora Using Wikidata

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  • Deep Learning for Retinal Optical Deflection

    A Unified Hypervolume Function for Fast Search and RetrievalSparse semantic segmentation from a dataset can be obtained from a text graph, by using a graph semantic graph (SVG). In this work, we present a new data visualization technique of the semantic graph as well as a simple feature extraction technique from graph graphs. In other words, the feature extraction method can be used to produce semantic segmentation results. The method is based on the idea of learning a graph representation of the semantic graph and learning a segmentation function to segment each node of the graph. Experimental results show that our algorithm can efficiently extract semantic segmentation results with very few parameters.


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