CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition

• URL: http://arxiv.org/abs/2204.12710v1
• Date: Wed, 27 Apr 2022 05:43:21 GMT
• Title: CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition
• Authors: Yu-Siou Tang and Chung-Hsien Wu
• Abstract summary: The CREER dataset uses the Stanford CoreNLP Annotator to capture rich language structures from Wikipedia plain text. This dataset follows widely used linguistic and semantic annotations so that it can be used for not only most natural language processing tasks but also scaling the dataset.
• Score: 9.54366784050374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe the design and use of the CREER dataset, a large corpus annotated with rich English grammar and semantic attributes. The CREER dataset uses the Stanford CoreNLP Annotator to capture rich language structures from Wikipedia plain text. This dataset follows widely used linguistic and semantic annotations so that it can be used for not only most natural language processing tasks but also scaling the dataset. This large supervised dataset can serve as the basis for improving the performance of NLP tasks in the future.

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