Improving Open Information Extraction via Iterative Rank-Aware Learning

@inproceedings{Jiang2019ImprovingOI,
  title={Improving Open Information Extraction via Iterative Rank-Aware Learning},
  author={Zhengbao Jiang and Pengcheng Yin and Graham Neubig},
  booktitle={ACL},
  year={2019}
}
Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extractions based on their estimated quality to adjust precision and recall of extracted assertions. We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences. We propose an… CONTINUE READING