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NLPContributionGraph -- Structuring Scholarly NLP Contributions in the Open Research Knowledge Graph

ncg-task.github.io · 3,029 words · saved by 1 readers

The NLPContributionGraph shared task comprises a dataset of Natural Language Processing scholarly articles annotated for their contributions. The annotations are provided in terms of three data elements: 1) contribution sentences; 2) scientific term and predicate phrases from the sentences; and 3) (subject,predicate,object) triple statements toward KG building, wherein the triples taken together form the articles' contribution-centered knowledge graph. In order to understand the dataset and the task better, we explain the annotated data with the help of examples. NLPContributionGraph uses two levels of knowledge systematization: 1) At the root, it has a dummy node called Contribution. And following the root node, 2) it has twelve nodes which we generically refer to as information units. Each scholarly article's annotated contribution triple statements are organized under three (mandatory) or more of these information unit nodes, depending on whether they apply to the article. These inf

NLPContributionGraph -- Structuring Scholarly NLP Contributions in the Open Research Knowledge Graph Dataset The NLPContributionGraph shared task comprises a dataset of Natural Language Processing scholarly articles annotated for their contributions. The annotations are provided in terms of three data elements: 1) contribution sentences; 2) scientific term and predicate phrases from the sentences; and 3) (subject,predicate,object) triple statements toward KG building, wherein the triples taken together form the articles' contribution-centered knowledge graph. In order to understand the dataset

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