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RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
TLDR
We propose RESIDE, a distantly-supervised neural relation extraction method which utilizes Graph Convolution Networks (GCN) to encode syntactic information from text for improved relation extraction. Expand
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CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
TLDR
We propose Canonicalization using Embeddings and Side Information (CESI) -- a novel approach which performs canonicalization over learned embeddings of Open KBs. Expand
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A Re-evaluation of Knowledge Graph Completion Methods
TLDR
We perform an extensive reexamination study of recent neural network based KGC techniques and find that many such models have issues with their score functions. Expand
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Composition-based Multi-Relational Graph Convolutional Networks
TLDR
We propose CompGCN, a novel Graph Convolutional framework for multi-relational graphs which jointly embeds both nodes and relations in a relational graph. Expand
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Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
TLDR
We propose SynGCN, a flexible Graph Convolution based method for learning word embeddings, which outperforms existing methods on various intrinsic and extrinsic tasks. Expand
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Attention Interpretability Across NLP Tasks
TLDR
The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque. Expand
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Dating Documents using Graph Convolution Networks
TLDR
We propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. Expand
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InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
TLDR
We propose InteractE, a model that applies convolutional filters on 2D reshapings of entity and relation embeddings in order to capture rich interactions between them. Expand
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Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
TLDR
We propose ConfGCN, a GCN framework for graph-based SSL, which estimates labels scores along with their confidences jointly in GCN-based setting. Expand
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Real Time Monitoring of Packet Loss in Software Defined Networks
TLDR
We present an approach for monitoring and measuring online per-flow and per-port packet loss and its comparison with some of the novel methods for measuring packet loss proposed for SDN and OpenFlow networks. Expand
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