Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities


Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities – This paper presents a new method of finding annotated sentences based on semantic labels for word pairs. Our approach consists of two parts: (1) a method for detecting when two sentences are alike by means of lexicon-based annotations of the same sentence pairs, and (2) a method to discover the semantic tag of the annotations for the two sentences that contains the same semantic tag. The tag of annotated sentences in some sentences can be inferred by means of lexicon-based annotations of the two sentences. Our first approach consists of two stages: (1) a method for identifying the semantic tag that contains the same semantic tag. (2) an algorithm that identifies the semantic tag for each sentence where the tags of the two sentences can be identified by means of lexicon and tag system. Using these two stages our method detects and aggregates annotated sentences for several sentences of different types including short sentences. Furthermore, we present a new method that automatically identifies and aggregates sentences for different kinds of sentences by means of lexicon-based annotations for each type of sentences.

This paper addresses the problem of high-dimensional high-resolution images. In this work, we propose a new deep nonlinear generative model to learn high-dimensional shape images by considering their temporal dynamics. We train the deep model via convolutional layers for predicting the shape features of the image by minimizing the reconstruction error. Our experiments show that our model provides high-resolution shape images with a rich temporal structure and can learn accurate predictions that outperform previous methods.

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Cross-Language Retrieval: An Algorithm for Large-Scale Retrieval Capabilities

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  • Including a Belief Function in a Deep Generative Feature Learning Network

    Convex Hulloo: Minimally Supervised Learning with Extreme Hulloo SearchThis paper addresses the problem of high-dimensional high-resolution images. In this work, we propose a new deep nonlinear generative model to learn high-dimensional shape images by considering their temporal dynamics. We train the deep model via convolutional layers for predicting the shape features of the image by minimizing the reconstruction error. Our experiments show that our model provides high-resolution shape images with a rich temporal structure and can learn accurate predictions that outperform previous methods.


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