Neural Open Relation Extraction via an Overlap-aware Sequence Tagging Scheme

@inproceedings{Jia2019NeuralOR,
  title={Neural Open Relation Extraction via an Overlap-aware Sequence Tagging Scheme},
  author={Shengbin Jia and E. Shijia and Yang Xiang},
  year={2019}
}
Solving the Open relation extraction (ORE) task with supervised neural networks, especially the neural sequence learning (NSL) models, is an extraordinarily promising way. However, there are three main challenges: (1) The lack of labeled training corpus; (2) Only one label is assigned to each word, resulting in being difficult to extract multiple, overlapping relations; (3) The confusion about the selection of various neural architectures for the ORE. In this paper, to overcome these challenges… CONTINUE READING

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