Papers with Open information extraction
Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open information extraction (OIE) is the task of extracting facts from natural language text. |
| Approach: | They propose a method for computing syntactically rich text embeddings using the structure of dependency trees and a discriminative approach to OIE where tokens in the generated fact are classified as "real" and "fake" they propose to reduce repetitive tokens and improve models' ability to generate implicit facts by a factor of 23%. |
| Outcome: | The proposed method reduces repetitive tokens by a factor of 23% on the CaRB, OIE2016, and LSOIE datasets and improves on augmented datasets. |
Improving Open Information Extraction via Iterative Rank-Aware Learning (P19-1)
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| Challenge: | Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. |
| Approach: | They propose an additional binary classification loss to calibrate the extraction likelihood . they propose an iterative learning process where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. |
| Outcome: | Experiments on open information extraction (IE) show that the extraction likelihood is not well calibrated when comparing quality of extracted assertions. |