Jun Zhao

Publications325
h-index45
Citations9,610
Highly Influential Citations1,119
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  • Publications
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Relation Classification via Convolutional Deep Neural Network
TLDR
In this paper, we exploit a convolutional deep neural network (DNN) to extract lexical and sentence level features for relation classification. Expand
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Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
TLDR
We propose a novel model dubbed Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning to address the two problems described above. Expand
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Recurrent Convolutional Neural Networks for Text Classification
TLDR
We introduce a recurrent convolutional neural network for text classification without human-designed features. Expand
  • 1,196
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Knowledge Graph Embedding via Dynamic Mapping Matrix
TLDR
In this paper, we propose a more fine-grained model named TransD, which is an improvement of TransR/CTransR. Expand
  • 581
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Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
TLDR
We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. Expand
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Collective entity linking in web text: a graph-based method
TLDR
This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Expand
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Inner Attention based Recurrent Neural Networks for Answer Selection
TLDR
We present three new RNN models that add attention information before RNN hidden representation, which shows advantage in representing sentence and achieves stateof-art results in answer selection task. Expand
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Learning to Represent Knowledge Graphs with Gaussian Embedding
TLDR
This paper switches to density-based embedding and propose KG2E for explicitly modeling the certainty of entities and relations, which learn the representations of KGs in the space of multi-dimensional Gaussian distributions. Expand
  • 188
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Knowledge Graph Completion with Adaptive Sparse Transfer Matrix
TLDR
We model knowledge graphs for their completion by encoding each entity and relation into a numerical space. Expand
  • 200
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Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
TLDR
We propose a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases. Expand
  • 196
  • 21
  • PDF