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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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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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Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
TLDR
We propose an end to end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of Normal, EntityPairOverlap and SingleEntiyOverlap. Expand
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CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning
TLDR
Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Expand
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Improving Question Retrieval in Community Question Answering Using World Knowledge
TLDR
We propose a way to build a concept thesaurus based on the semantic relations extracted from the world knowledge of Wikipedia. Expand
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Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
TLDR
The multiple relation extraction task tries to extract all relational facts from a sentence. Expand
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Adversarial learning for distant supervised relation extraction
Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier withExpand
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Deep Learning-Based Data Storage for Low Latency in Data Center Networks
TLDR
We utilize a deep-learning technology to help the data center networks (DCNs) learn from historical access information and make optimal data storage decision. Expand
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Towards faster and better retrieval models for question search
TLDR
We propose a faster and better retrieval model for question search by leveraging user chosen category. Expand
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