@article{Zhang2018, 
author = {Runyan Zhang and Fanrong Meng and Yong Zhou and Bing Liu},
title = {Relation Classification via Recurrent Neural Network with Attention and Tensor Layers},
year = {2018},
journal = {Big Data Mining and Analytics},
volume = {1},
number = {3},
pages = {234-244},
keywords = {semantic relation classification, bidirectional Recurrent Neural Network (RNNs), attention mechanism, neural tensor networks},
url = {https://www.sciopen.com/article/10.26599/BDMA.2018.9020022},
doi = {10.26599/BDMA.2018.9020022},
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.}
}