Pronoun Disambiguation from Phrase, XML and database Examples


Pronoun Disambiguation from Phrase, XML and database Examples – This paper presents a novel and efficient method for identifying, grouping and prioritizing linguistic data over a given corpus. We use a novel clustering methodology based on clustering and feature selection. We use the clustering parameters as a metric and use the resulting ranking of words to predict the cluster likelihood of each word. We then use the clustering process to categorize the clusters in a hierarchy. We evaluate the performance of this approach on a set of 20k words from the WMT corpus. As a case study, we use the corpus to perform a semantic distance test in which we compare the performance of a cluster-based approach. The performance of this approach can be very good because of the clustering method. Our approach can be easily extended to multiple applications such as the word-sentence semantic clustering as well as linguistic clustering which has strong advantages.

We present a novel architecture for learning algorithms to predict future actions by solving a stochastic optimization problem. Using the existing algorithms’ optimal algorithms, our new algorithms learn efficiently and efficiently to solve the stochastic optimization problem. We show that by using this architecture, the new algorithms can be used as model-free and as a principled approach to the problem of optimization of action outcomes. We show that the proposed algorithm can be used in multiple tasks to learn a new task-specific strategy, which is then used to optimize a new action. Experiments on two datasets demonstrate the superior performance of our new algorithm compared to existing strategies.

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Pronoun Disambiguation from Phrase, XML and database Examples

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    An Improved Algorithm for Optimizing Expectation through Reinforcement LearningWe present a novel architecture for learning algorithms to predict future actions by solving a stochastic optimization problem. Using the existing algorithms’ optimal algorithms, our new algorithms learn efficiently and efficiently to solve the stochastic optimization problem. We show that by using this architecture, the new algorithms can be used as model-free and as a principled approach to the problem of optimization of action outcomes. We show that the proposed algorithm can be used in multiple tasks to learn a new task-specific strategy, which is then used to optimize a new action. Experiments on two datasets demonstrate the superior performance of our new algorithm compared to existing strategies.


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