Conjectured Relation to Learned Neural Network Models
A Dependency Based Neural Network for Relation Classification
... We adapt the augmented dependency path into a dependency subtree and apply DT-RNN. As shown in Table 2, DepNN achieves the best result (83.6) using NER features. WordNet fea- tures can also improve the performance of ...
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Convolutional Neural Network Language Models
... Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision ...needs models to capture local as well as long-range dependency infor- ...language models, ...
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Relation Classification via Convolutional Deep Neural Network
... vector models are severely limited because they do not capture long distance features and semantic com- positionality, the important quality of natural language that allows humans to understand the meanings of a ...
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Neural Network Approaches to Implicit Discourse Relation Recognition
... discourse relation (IDR) with neural networks, in particular with convolutional (CNN) and recurrent neural networks ...penultimate network layer operates on information which is not ...
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Paraphrastic language models and combination with neural network language models
... AND RELATION TO PRIOR WORK This paper investigated using statistical paraphrase approach to im- prove the context coverage and generalization of n-gram LMs for Mandarin Chinese broadcast speech ...
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A Systematic Study of Neural Discourse Models for Implicit Discourse Relation
... ral network models have been proposed to tackle this ...propose neural net- work models that are based on feedfor- ward and long-short term memory archi- tecture and systematically study the ...
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Slow dynamics in structured neural network models
... 6.2.3 Learning asymmetric biases In the previous section we showed that a hierarchical hidden structure provides a flexible way to generate complex sequences in a compact form thanks to the possibility of reusing ...
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A survey on neural network models for data analysis
... LVQ network firstly has a competitive layer and then a second linear ...classes learned by the competitive layer are referred to as subclasses and the classes of the linear layer as target ...the ...
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Lipreading with convolutional and recurrent neural network models
... This is called a convolution. Filter parameters f ab can be learned to represent the features of the image. Compared to fully connected layers, in convolutional layers the features are extracted more effectively ...
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Watermarking Federated Deep Neural Network Models
... Specifically, a neural network is composed of several layers. Each layer is made of several neurons, that actually have the computational capability. As shown in Figure 1, a neuron combines inputs from the ...
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Paraphrastic recurrent neural network language models
... Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recogni- tion ...itly learned by ...
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Deep Structured Neural Network for Event Temporal Relation Extraction
... this relation based on their knowledge in history, but it is difficult for ma- chines without prior ...Our models do not pick up this signal and hence pre- dict the relation as ...
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Attention Based Convolutional Neural Network for Semantic Relation Extraction
... Nowadays, neural networks play an important role in the task of relation ...convolutional neural network architecture for this ...of-the-art neural network models and can ...
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Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
... model can also serve as one of the base models in the ensemble. We trained DANN to read the development set of bc to adapt to this domain. Although the gain seems to be small, the improvement is statistically ...
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Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition
... SVM-based models, RAE per- forms poorly in three relations, except E XP ...SCNN models perform remark- ably well, producing comparable and even bet- ter ...
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GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
... CNN-based network, i.e., gated relation network (GRN), for named entity recogni- tion ...gated relation layer to model the relations between any two words, and utilizes gating mechanism to ...
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The novel Artificial Neural Network assisted models: A review
... of neural biological networks is ...biological neural network. An artificial neural network consists of many very basic and interconnected processors, also known as neurons, identical ...
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Structural combination of neural network models
... Both the individual NNs and the CB algorithms were imple- mented in Matlab R 2010 using its neural networks toolbox. D. Structural combination based on genetic algorithms The cluster-based implementation described ...
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Clustering ensembles of neural network models
... At this point we wish to underline that in the present article we do not claim to outperform the above mentioned authors in terms of prediction error. The present article is meant to show that model clustering, through ...
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Deep Neural Network Language Models
... deep neural networks. We followed the feed-forward neural network architecture and made the network deeper with the addition of several lay- ers of ...
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