semantic relation classification, bidirectional Recurrent Neural Network (RNNs), attention mechanism, neural tensor networks
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.
Tsinghua University Press
Runyan Zhang, Fanrong Meng, Yong Zhou et al. Relation Classification via Recurrent Neural Network with Attention and Tensor Layers. Big Data Mining and Anyalytics 2018, 01(03): 234-244.