Jan 20, 2018 · State-of-the-art methods are mainly based on deep neural networks. This paper proposes a new convolutional neural network (CNN) architecture ...
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This paper proposes a new convolutional neural network (CNN) architecture which combines the syntactic tree structure and other lexical level features ...
“Embedding Syntactic Tree Structures into CNN Architecture for Relation Classification” is a paper by Feiliang Ren Rongsheng Zhao Xiao Min Hu Yongcheng Li ...
AbstractElectromigration experiments have been carried out on simple Cu dual-damascene interconnect tree structures consisting of straight via-to-via (or ...
Sep 7, 2017 · In this paper, we adopt similar ideas but apply them to a neural attention model for question answering. The constituency tree (Manning et al., ...
This paper proposes a new convolutional neural network (CNN) architecture which combines the syntactic tree structure and other lexical level features together ...
In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree.
This paper exploits a convolutional deep neural network (DNN) to extract lexical and sentence level features from the output of pre-existing natural ...
In this work, we propose a new convolutional neural network (CNN), which we name Classifi- cation by Ranking CNN (CR-CNN), to tackle the relation classification ...
Beside CNN models that incorporate syntactic knowledge in their embeddings, other approaches proposed neural networks (NN) in which the topology is adapted to ...