Supervised Neural Models Revitalize the Open Relation Extraction

@article{Jia2018SupervisedNM,
  title={Supervised Neural Models Revitalize the Open Relation Extraction},
  author={Shengbin Jia and Yang Xiang and Xiaojun Chen},
  journal={ArXiv},
  year={2018},
  volume={abs/1809.09408}
}
  • Shengbin Jia, Yang Xiang, Xiaojun Chen
  • Published 2018
  • Computer Science
  • ArXiv
  • Open relation extraction (ORE) remains a challenge to obtain a semantic representation by discovering arbitrary relation tuples from the un-structured text. However, perhaps due to limited data, previous extractors use unsupervised or semi-supervised methods based on pattern matching, which heavily depend on manual work or syntactic parsers and are inefficient or error-cascading. Their development has encountered bottlenecks. Although a few people try to use neural network based models to… CONTINUE READING

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    Language-consistent Open Relation Extraction: from Multilingual Text Corpora

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    LOREM: Language-consistent Open Relation Extraction from Unstructured Text

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    Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data

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    CaRB: A Crowdsourced Benchmark for Open IE

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