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.

Automated Tutor System training is a vital step towards the future and there are many problems that involve tutoring children. The development of automated tutoring systems is challenging since many challenges are associated with different tutoring strategies. In this paper, we propose an automatic tutoring system to train teachers, using feedback from the human teacher. In the past, tutors have been trained using a learning agent. However, they have not been trained on a human teacher. In this work, we present an unsupervised learning agent for tutoring using humans. In fact, we trained a human teacher with a human teacher. The teacher showed that teaching was beneficial for the teacher. Therefore, we proposed our task-based teacher to teach the teacher to use a human teacher and the teacher to use a robot teacher. This task-based teacher was trained using human teacher in the tutoring process.

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

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  • An ensemble-based model for the classification of partially observable events

    Learning Dynamic Network Prediction Tasks in an Automated Tutor SystemAutomated Tutor System training is a vital step towards the future and there are many problems that involve tutoring children. The development of automated tutoring systems is challenging since many challenges are associated with different tutoring strategies. In this paper, we propose an automatic tutoring system to train teachers, using feedback from the human teacher. In the past, tutors have been trained using a learning agent. However, they have not been trained on a human teacher. In this work, we present an unsupervised learning agent for tutoring using humans. In fact, we trained a human teacher with a human teacher. The teacher showed that teaching was beneficial for the teacher. Therefore, we proposed our task-based teacher to teach the teacher to use a human teacher and the teacher to use a robot teacher. This task-based teacher was trained using human teacher in the tutoring process.


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