Categorization with Linguistic Network and Feature Representation


Categorization with Linguistic Network and Feature Representation – The article provides a new way of learning language semantics and an experimental evaluation on the task of categorization of Chinese vocabulary with the purpose of further understanding its usage in the social domains. We performed a comparative study on some benchmark corpora of Chinese vocabulary with their semantic meanings and the use of semantic features in sentence categorization. The results show that our method outperforms state-of-the-art methods by a wide margin.

We propose a deep learning for intelligent machine interaction that aims to be both adaptive and non-adaptive to the interaction of individuals. Based on this approach, multiple machines collaborate to solve a long-running challenge by predicting and learning from input images of interacting individuals, which can be used for the first task of collaborative navigation. Our experimental results demonstrate that as a result of our approach, the two machines converge to a global agreement on the task and that the two machines perform well under different policies.

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Categorization with Linguistic Network and Feature Representation

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  • G-CNNs for Classification of High-Dimensional Data

    Lifelong Learning for Answerability EducationWe propose a deep learning for intelligent machine interaction that aims to be both adaptive and non-adaptive to the interaction of individuals. Based on this approach, multiple machines collaborate to solve a long-running challenge by predicting and learning from input images of interacting individuals, which can be used for the first task of collaborative navigation. Our experimental results demonstrate that as a result of our approach, the two machines converge to a global agreement on the task and that the two machines perform well under different policies.


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