CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition
This provides a foundational resource for researchers in natural language processing to improve model performance, though it is incremental as it builds on existing annotation methods.
The authors introduced CREER, a large-scale corpus annotated with English grammar and semantic attributes using Stanford CoreNLP, derived from Wikipedia text to support various NLP tasks and enable dataset scaling.
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. We publicize the dataset through the link: https://140.116.82.111/share.cgi?ssid=000dOJ4