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Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to
doi:10.24963/ijcai.2018/610
dblp:conf/ijcai/SuJ0ZL18
fatcat:jgshpwceavcwjkbv4mr4unw4s4