LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction
Jacob Solawetz, Stefan Larson
Information Extraction and Text Mining Short paper Paper
You can open the pre-recorded video in separate windows.
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.
NOTE: Video may display a random order of authors.
Correct author list is at the top of this page.