A Semantics of Text


A Semantics of Text – We consider a probabilistic framework for the task of lexical content prediction. While previous work used the word-level context of the word or a collection of words to predict word-level word embeddings, this work builds on the concept of a word-level context that provides different types of contextual information into the problem. We present a new framework, called word-level context for lexical content prediction. This technique can be used to model the lexical content that is being predicted from a given context and, even without using any word-level context, we can improve the performance of the task.

We propose a new stochastic algorithm for supervised learning. The key idea is to split the supervised learning problem in two, and learn the supervised class from both these split problems. The solution is a two-step process, in which each step is performed by using a set of convolutional features. The learned structures are fed to the supervised learning algorithm using a multi-dimensional metric, and the weights of the trained supervised class are computed, each weight being weighted by the sum of two weight matrices. We test our technique on the ImageNet dataset of images of humans and animals taken over a six week period. Our method outperforms both supervised clustering algorithms and an earlier algorithm. Additionally, it scales well to synthetic and real-world datasets, and has been observed to converge to a much lower number of clusters than the state-of-the-art stochastic gradient descent algorithm.

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A Semantics of Text

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  • Learning from Past Profiles

    On the convergence of conditional variable clustering methodsWe propose a new stochastic algorithm for supervised learning. The key idea is to split the supervised learning problem in two, and learn the supervised class from both these split problems. The solution is a two-step process, in which each step is performed by using a set of convolutional features. The learned structures are fed to the supervised learning algorithm using a multi-dimensional metric, and the weights of the trained supervised class are computed, each weight being weighted by the sum of two weight matrices. We test our technique on the ImageNet dataset of images of humans and animals taken over a six week period. Our method outperforms both supervised clustering algorithms and an earlier algorithm. Additionally, it scales well to synthetic and real-world datasets, and has been observed to converge to a much lower number of clusters than the state-of-the-art stochastic gradient descent algorithm.


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