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.

Deep learning (DL) has been shown to perform well despite its limited training data. In this work we extend the DL to learning conditional gradient descent (CLG). To handle the problem of not having any explicit input, we use a pre-trained neural network, and perform a supervised method for the task. Our method learns the distribution of all the variables in the dataset at the same time, to ensure the correct representation of the data in the first place. To handle the non-classicalities of data, we use a pre-trained convolutional neural network to learn the distribution of the variables in the data. This approach is used to extract a latent-variable model from the output of the network. We have used this model and the distribution of the variables to build the model for each training sample. We empirically show that in real-world applications we can achieve better performance, by training the network on single samples, rather than on samples with variable sizes. We also demonstrate the effectiveness of the proposed method via simulated examples.

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

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    Pseudo Generative Adversarial Networks: Learning Conditional Gradient and Less-Predictive ParameterDeep learning (DL) has been shown to perform well despite its limited training data. In this work we extend the DL to learning conditional gradient descent (CLG). To handle the problem of not having any explicit input, we use a pre-trained neural network, and perform a supervised method for the task. Our method learns the distribution of all the variables in the dataset at the same time, to ensure the correct representation of the data in the first place. To handle the non-classicalities of data, we use a pre-trained convolutional neural network to learn the distribution of the variables in the data. This approach is used to extract a latent-variable model from the output of the network. We have used this model and the distribution of the variables to build the model for each training sample. We empirically show that in real-world applications we can achieve better performance, by training the network on single samples, rather than on samples with variable sizes. We also demonstrate the effectiveness of the proposed method via simulated examples.


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