Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech Tagging


Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech Tagging – There are two major challenges involved in using this model: 1) the temporal relationships between words of the input text; 2) the fact that text and sentences are not independent. In practice, this can be addressed as a two-stream temporal model for finding meaningful associations between words in an input text, and by using the proposed multi-channel recurrent neural network. Several experiments have been conducted on four related tasks: semantic segmentation, topic modeling, recognition and classification. The performance of the proposed multi-channel neural network is comparable to CNNs for semantic segmentation tasks. The results are compared with CNNs and DNNs for semantic segmentation tasks and have very good results.

We propose a novel and practical method to classify road signs. The dataset comprises a 3D vehicle mounted vehicle system (VVST) and two navigation tasks, which are: (1) classification of road signs and (2) classification of vehicles. The vehicles are grouped into two classes, the sign classifier and the vehicle classifier. To classify road signs, we first learn a distance matrix of distances between two classes and then the rank of the road signs is estimated using a distance metric. Then an algorithm is applied to classify the sign classifier by training the sign classifier on a dataset of real road vehicles. In this paper, we will discuss the results.

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Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech Tagging

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  • Tensorizing the Loss Weight for Accurate Multi-label Speech Recognition

    A Multi-View Hierarchical Clustering Framework for Optimal Vehicle RoutingWe propose a novel and practical method to classify road signs. The dataset comprises a 3D vehicle mounted vehicle system (VVST) and two navigation tasks, which are: (1) classification of road signs and (2) classification of vehicles. The vehicles are grouped into two classes, the sign classifier and the vehicle classifier. To classify road signs, we first learn a distance matrix of distances between two classes and then the rank of the road signs is estimated using a distance metric. Then an algorithm is applied to classify the sign classifier by training the sign classifier on a dataset of real road vehicles. In this paper, we will discuss the results.


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