Learning Dependency Trees for Automatic Evaluation of Social Media Influences


Learning Dependency Trees for Automatic Evaluation of Social Media Influences – Many existing semantic and retrieval systems rely on the knowledge that user-sentences are similar and therefore have similar semantic relations. This paper first provides an overview of the semantic relations between user-sentences based on the two datasets of the literature. In particular, we present a semantic relations network for sentiment classification and summarization of users-sentences. Further, we describe the semantics of user-sentences, and compare the semantic relations between user-sentences to their relational relations. Finally, this paper proposes the first semantic relations network for the semantic relations between user-sentences. Our experiments show that using semantic relations based on the semantic relations network improves classification performance in the context of both human and computer experts.

We present a novel method for inferring the probability distribution of a pair of variables by performing an optimal estimation of a covariance matrix. The method does not use the exact covariance matrix as the only relevant information that is needed to infer the covariance matrix. Instead, our method computes a posterior distribution over the covariance matrix of the variables of interest. The covariance matrix is then used to infer the posterior distribution of the variables of interest. Our method is applicable on high-dimensional data sets and does not require any prior knowledge on the covariance matrix. We show that our method performs well, and its performance has a significant impact on the likelihood of the model being an accurate one.

Multi-Instance Dictionary Learning for Classification and Segmentation

Fast Recurrent Neural Networks for Video Generation

Learning Dependency Trees for Automatic Evaluation of Social Media Influences

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  • An Adaptive Algorithm for the Nonnegative Matrix Factorization

    Konstantin Yarosh’s Theorem of Entropy and Cognate InformationWe present a novel method for inferring the probability distribution of a pair of variables by performing an optimal estimation of a covariance matrix. The method does not use the exact covariance matrix as the only relevant information that is needed to infer the covariance matrix. Instead, our method computes a posterior distribution over the covariance matrix of the variables of interest. The covariance matrix is then used to infer the posterior distribution of the variables of interest. Our method is applicable on high-dimensional data sets and does not require any prior knowledge on the covariance matrix. We show that our method performs well, and its performance has a significant impact on the likelihood of the model being an accurate one.


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