Neural-based Word Sense Disambiguation with Knowledge-base Fusion – The recently proposed task-based evaluation and recognition systems, such as the word sense recognition approach, or the word pair-based evaluation framework, have been shown to benefit from semantic information such as speaker attributes and sentence-level lexical resources. We present a learning based evaluation framework for a combination of these two tasks, which use semantic information for the evaluation of each task. We propose the evaluation framework as a novel semantic evaluation model, which learns to recognize a phrase, using its speaker attributes and sentence-level lexical resources. Additionally, we extend the evaluation model to classify phrase pairs as a sequence of phrase pairs (as opposed to a list of phrase pairs), which allows us to use semantic resources for this task. Our evaluation results show that the recognition, recognizing, and ranking of phrase pairs are significantly improved.

In this paper, we present a new probabilistic model class, which is the same as classical logistic regression models and yet is better general. In previous work, we used Bayesian network and model parameters to model the problem of estimating the unknowns from the data. In this paper, we extend the Bayesian network model with a regularization function (in terms of the maximum of these parameters) to the latent variable model (in terms of the model parameters). For more generalization, we provide a new model class named Bayesian networks. The model is learned in three steps: a Bayesian network model model with a regularized parameter, a regularized model model with a belief propagation function that learns to generate more information in the form of a belief matrix, as well as a probability distribution model. The model is proved to represent the empirical data, an empirical data set, and the data set. Our proposed method is implemented on four real and several data sets.

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# Neural-based Word Sense Disambiguation with Knowledge-base Fusion

A Generalization of the $k$-Scan Sampling Algorithm for Kernel Density Estimation

Probabilistic Latent Variable ModelsIn this paper, we present a new probabilistic model class, which is the same as classical logistic regression models and yet is better general. In previous work, we used Bayesian network and model parameters to model the problem of estimating the unknowns from the data. In this paper, we extend the Bayesian network model with a regularization function (in terms of the maximum of these parameters) to the latent variable model (in terms of the model parameters). For more generalization, we provide a new model class named Bayesian networks. The model is learned in three steps: a Bayesian network model model with a regularized parameter, a regularized model model with a belief propagation function that learns to generate more information in the form of a belief matrix, as well as a probability distribution model. The model is proved to represent the empirical data, an empirical data set, and the data set. Our proposed method is implemented on four real and several data sets.