Semantic Word Segmentation in Tag-line Search


Semantic Word Segmentation in Tag-line Search – Word embeddings are an important statistical tool in many applications including human-computer interaction and natural language processing systems. In this work, we show that one-way word embeddings enable semantic segmentation of multiple words, and that this segmentation results in the segmentation of phrases with multiple entities that were not considered previously in the word embeddings. To this end, we propose a novel approach for this task, which leverages the semantic word embeddings. Our experimental results show that our model outperforms state-of-the-art approaches by a large margin on various benchmarks.

We present a novel algorithm based on the observation that a sparse mixture of image sequences is a better fit than a multilinear mixture, which is an existing popular image classification method. Our algorithm first uses the image data as a prior and then uses a pair of images to model the input vector. Our method utilizes a combination of pairwise similarity as well as dictionary learning which consists of two components. The first component is an image representation that is considered as a subspace for image data. The second component is a pairwise similarity representation that learns a similarity matrix between them. This matrix matrix is learned using a variational inference-based Bayesian network (Bayesian Network) that is trained on image pairs and evaluated on a single image. We demonstrate that, by relying on pairwise similarity and dictionary learning, our algorithm is able to obtain high-quality classification results while significantly reducing the number of training samples compared to previous methods.

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Semantic Word Segmentation in Tag-line Search

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  • HOG: Histogram of Goals for Human Pose Translation from Natural and Vision-based Visualizations

    A Novel Feature Selection Method Based On Bayesian Network Approach for Image SegmentationWe present a novel algorithm based on the observation that a sparse mixture of image sequences is a better fit than a multilinear mixture, which is an existing popular image classification method. Our algorithm first uses the image data as a prior and then uses a pair of images to model the input vector. Our method utilizes a combination of pairwise similarity as well as dictionary learning which consists of two components. The first component is an image representation that is considered as a subspace for image data. The second component is a pairwise similarity representation that learns a similarity matrix between them. This matrix matrix is learned using a variational inference-based Bayesian network (Bayesian Network) that is trained on image pairs and evaluated on a single image. We demonstrate that, by relying on pairwise similarity and dictionary learning, our algorithm is able to obtain high-quality classification results while significantly reducing the number of training samples compared to previous methods.


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