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• W3153140595 abstract "Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset LSOIE. Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available." @default.
• W3153140595 created "2021-04-26" @default.
• W3153140595 creator A5023740689 @default.
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• W3153140595 date "2021-01-01" @default.
• W3153140595 modified "2023-09-26" @default.
• W3153140595 title "LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction" @default.
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• W3153140595 doi "https://doi.org/10.18653/v1/2021.eacl-main.222" @default.
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