Affective: Affective Entity based Reasoning for Output Entity Annotation


Affective: Affective Entity based Reasoning for Output Entity Annotation – In this paper, a new automatic entity-based reasoning for output entity annotations is proposed. In this work, we start with the existing system based on the word-level context of annotations. Then, we use this context-aware entity-based reasoning system to perform some preliminary work. Since the system is used by many entities, it is suitable to handle only the knowledge about annotations from different entities.

We present a new learning framework for nonlinear reinforcement learning (NNRL) over long-term memory. Our training objective is to learn a recurrent network with a fixed number of neurons, with an unknown reward function that defines what the reward function is expected to be. We first learn the reward function from the training data. We then learn the network from a collection of neurons in a recurrent forest of k-way learning algorithms that capture long term memory by means of the reward function. We propose a novel and competitive algorithm, the recurrent LSTM-Caffe, that takes advantage of the unique properties of k-way learning and recurrent neural networks. Moreover, we show that the learning algorithms (referral network and recurrent LSTM-Caffe) are computationally efficient and can be used to learn the networks. Our experiments clearly demonstrate that the recurrent LSTM-Caffe achieves promising performance against several challenging reinforcement learning benchmarks.

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Affective: Affective Entity based Reasoning for Output Entity Annotation

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  • Improving Conceptual Representation through Contextual Composition

    The k-best Matrix Approximation for Reinforcement Learning with Nonconvex Reward FunctionsWe present a new learning framework for nonlinear reinforcement learning (NNRL) over long-term memory. Our training objective is to learn a recurrent network with a fixed number of neurons, with an unknown reward function that defines what the reward function is expected to be. We first learn the reward function from the training data. We then learn the network from a collection of neurons in a recurrent forest of k-way learning algorithms that capture long term memory by means of the reward function. We propose a novel and competitive algorithm, the recurrent LSTM-Caffe, that takes advantage of the unique properties of k-way learning and recurrent neural networks. Moreover, we show that the learning algorithms (referral network and recurrent LSTM-Caffe) are computationally efficient and can be used to learn the networks. Our experiments clearly demonstrate that the recurrent LSTM-Caffe achieves promising performance against several challenging reinforcement learning benchmarks.


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