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Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of twoExpand
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A Convolutional Attention Network for Extreme Summarization of Source Code
Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariantExpand
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Discriminative Neural Sentence Modeling by Tree-Based Convolution
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling. Our models leverage either constituency trees or dependency trees of sentences. TheExpand
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Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with aExpand
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Reliability Analysis for Multi-Component Systems Subject to Multiple Dependent Competing Failure Processes
For complex multi-component systems with each component experiencing multiple failure processes due to simultaneous exposure to degradation and shock loads, we developed a new multi-component systemExpand
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Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN
Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are aExpand
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SLOMS: A Privacy Preserving Data Publishing Method for Multiple Sensitive Attributes Microdata
Multi-dimension bucketization is a typical method to anonymize multiple sensitive attributes. However, the method leads to low data utility when microdata have more sensitive attributes. In addition,Expand
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Component Reliability Criticality or Importance Measures for Systems With Degrading Components
This paper proposes two new importance measures: one new importance measure for systems with -independent degrading components, and another one for systems with -correlated degrading components.Expand
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Learning Joint Semantic Parsers from Disjoint Data
We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap. We handleExpand
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A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neuralExpand
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