Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction (2020.emnlp-main)
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| Challenge: | Open information extraction (OIE) is a method for extracting facts from text in structured format . alternative formulations allow for longer tuples, but most work focuses on binary predicates only. |
| Approach: | They propose to extract facts from natural language text and represent them as structured triples . they compare different neural network architectures and training approaches . |
| Outcome: | The proposed approach improves the currently best models on the OIE16 benchmark by 0.421 F1 score and 0.420 AUC-PR . |
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| Challenge: | Open Information Extraction (OpenIE) aims to generate structured tuples from unstructured open-domain text. |
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| Challenge: | Open Information Extraction (OIE) is a structure prediction task in NLP that aims to extract structured n-ary tuples from free text. |
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