Abstract
Relation classification is an important research task in the field of natural language processing (NLP). In this paper, we apply a bidirectional recurrent neural network upon local windows of entities for relation classification. In contrast to previous approaches, only word tokens around entities are taken into consideration in our model. Upon word tokens, a bidirectional recurrent neural network is used to extract local context features of entities. To retain the important features for classification , we propose to use a novel weighted pooling layer upon hidden layers of RNN. Experiments on the SemEval-2010 dataset show that our proposed method achieves competitive results without introducing any external resources.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (61472428, 61003204), the Fundamental Research Funds for the Central Universities, the Research Funds of Renmin University of China No. 14XNLQ06 and Tencent company.
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Hou, X., Zhao, Z., Liu, T., Du, X. (2016). Classifying Relation via Bidirectional Recurrent Neural Network Based on Local Information. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_34
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DOI: https://doi.org/10.1007/978-3-319-45814-4_34
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