A Novel Approach to Text Classification based on Keyphrase Matching and Word Translation


A Novel Approach to Text Classification based on Keyphrase Matching and Word Translation – In many languages the choice of a common language partner has significant impact on the quality of a text. Here we propose a language-independent method that extracts the most useful information from text. This method is based on an evolutionary algorithm to select a partner that best captures the needs of a text. The key to this approach is the combination of two key features: (1) the target language partner with the most resources is the same language partner, and (2) the candidate partner is an intelligent agent. Our method, termed as a bilingual text classifier (BLCS), extracts the most relevant information and the most useful information from the candidate partner, based on a genetic algorithm’s approach of evolutionary design. Through experiments on both simulated and real data it was shown that it is possible to significantly improve the quality of a text, in terms of both the resources and the candidate partner for each language partner.

We report on the development of the proposed multinomial family of probabilistic models, and a comparison of their properties against the existing ones. We prove that the Bayesian multinomial family of probabilistic models is not a linear combination of two functions which is the case in both the linear family of models and the linear model by a new family of parameters. More precisely, we prove that the Bayesian multinomial family of probabilistic models is, given a set of functions of the same form, not a linear combination of a function of a function from multiple functions, which is the case in both the linear family of models and the linear model by a new family of parameters.

Deep Multi-view Feature Learning for Text Recognition

Anomaly Detection with Neural Networks and A Discriminative Labeling Policy

A Novel Approach to Text Classification based on Keyphrase Matching and Word Translation

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  • Inter-rater Agreement at Spatio-Temporal-Sparsity-Regular and Spatio-Temporal-Sparsity-Normal Sparse Signatures

    Probabilistic Models for Robust Machine LearningWe report on the development of the proposed multinomial family of probabilistic models, and a comparison of their properties against the existing ones. We prove that the Bayesian multinomial family of probabilistic models is not a linear combination of two functions which is the case in both the linear family of models and the linear model by a new family of parameters. More precisely, we prove that the Bayesian multinomial family of probabilistic models is, given a set of functions of the same form, not a linear combination of a function of a function from multiple functions, which is the case in both the linear family of models and the linear model by a new family of parameters.


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