Domain-Targeted, High Precision Knowledge Extraction

@article{Mishra2017DomainTargetedHP,
  title={Domain-Targeted, High Precision Knowledge Extraction},
  author={Bhavana Dalvi Mishra and Niket Tandon and Peter Clark},
  journal={Transactions of the Association for Computational Linguistics},
  year={2017},
  volume={5},
  pages={233-246}
}
Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To… CONTINUE READING

Results and Topics from this paper.

Key Quantitative Results

  • we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80% precision and 23% recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources.
  • First, we present a high precision extraction pipeline able to extract (subject,predicate,object) tuples relevant to a domain with precision in excess of 80%.

Citations

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SEVA: A Systems Engineer's Virtual Assistant

  • AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering
  • 2019
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