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Relation classification is a crucial component in many Natural Language Processing (NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture (using Long Short-Term Memory, LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8 dataset show that our model outperforms most state-of-the-art methods.


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Relation Classification via Recurrent Neural Network with Attention and Tensor Layers

Show Author's information Runyan ZhangFanrong Meng( )Yong ZhouBing Liu
China University of Mining and Technology, Xuzhou 210009, China.

Abstract

Relation classification is a crucial component in many Natural Language Processing (NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture (using Long Short-Term Memory, LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8 dataset show that our model outperforms most state-of-the-art methods.

Keywords:

semantic relation classification, bidirectional Recurrent Neural Network (RNNs), attention mechanism, neural tensor networks
Received: 03 February 2018 Accepted: 01 March 2018 Published: 24 May 2018 Issue date: September 2018
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Publication history

Received: 03 February 2018
Accepted: 01 March 2018
Published: 24 May 2018
Issue date: September 2018

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© The author(s) 2018

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61572505) and ChanXueYan Prospective Project of Jiangsu Province (No. BY2015023-05).

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