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Relation Classification via Recurrent Neural Network with Attention and Tensor Layers
Big Data Mining and Analytics 2018, 1 (3): 234-244
Published: 24 May 2018
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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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