natural-language-processing-2
Hyper-Relational Extraction
3260 papers • 126 benchmarks • 313 datasets
HyperRED is a dataset for the new task of hyper-relational extraction, which extracts relation triplets together with qualifier information such as time, quantity or location. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967).
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A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach
Soujanya Poria, Lidong Bing, Luo Si, Sharifah Mahani Aljunied, Yew Ken Chia•Thu Nov 17 2022
This work proposes CubeRE, a cube-filling model inspired by table- filling approaches and explicitly considers the interaction between relation triplets and qualifiers, and further proposes acube-pruning method to improve model scalability and reduce negative class imbalance.
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