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Open Access

Relation Classification via Recurrent Neural Network with Attention and Tensor Layers

China University of Mining and Technology, Xuzhou 210009, China.
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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 classificationbidirectional Recurrent Neural Network (RNNs)attention mechanismneural tensor networks

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Big Data Mining and Analytics
Volume 1 Issue 3,
September 2018
Pages 234-244
DOI: 10.26599/BDMA.2018.9020022
Cite this article:
Zhang R, Meng F, Zhou Y, et al. Relation Classification via Recurrent Neural Network with Attention and Tensor Layers. Big Data Mining and Analytics, 2018, 1(3): 234-244. https://doi.org/10.26599/BDMA.2018.9020022

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Received: 03 February 2018
Accepted: 01 March 2018
Published: 24 May 2018
© The author(s) 2018
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