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In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of Graph Convolutional Networks (GCNs). We perform a comparison among different Named Entity Recognition (NER) architectures and show that the grammar of a sentence positively influences the results. Experiments on the OntoNotes 5.0 dataset demonstrate consistent performance improvements, without requiring heavy feature engineering nor additional language-specific knowledge 1 .

In clinical neuroimaging applications where subjects belong to one of multiple classes of disease states and multiple imaging sources are available, the aim is to achieve accurate classification while assessing the importance of the sources in the classification task. This work proposes the use of fully Bayesian multiple-class multiple-kernel learning based on Gaussian Processes, as it offers flexible classification capabilities and a sound quantification of uncertainty in parameter estimates and predictions. The exact inference of parameters and accurate quantification of uncertainty in Gaussian Process models, however, poses a computationally challenging problem. This paper proposes the application of advanced inference techniques based on Markov chain Monte Carlo and unbiased estimates of the marginal likelihood, and demonstrates their ability to accurately and efficiently carry out inference in their application on synthetic data and real clinical neuroimaging data. The results in this paper are important as they further work in the direction of achieving computationally feasible fully Bayesian models for a wide range of real world applications.

For Intelligent Assistants (IA), user activity is often used as a lag metric for user satisfaction or engagement. Conversely, predictive leading metrics for engagement can be helpful with decision making and evaluating changes in satisfaction caused by new features. In this paper, we propose User Return Time (URT), a fine grain metric for gauging user engagement. To compute URT, we model continuous inter-arrival times between users' use of service via a log Gaussian Cox process (LGCP), a form of inhomogeneous Poisson process which captures the irregular variations in user usage rate and personal preferences typical of an IA. We show the effectiveness of the proposed approaches on predicting the return time of users on real-world data collected from an IA. Experimental results demonstrate that our model is able to predict user return times reasonably well and considerably better than strong baselines that make the prediction based on past utterance frequency.