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

TL;DR

CREER is a large, richly annotated corpus derived from Wikipedia, designed to enhance relation extraction and entity recognition tasks in NLP by providing comprehensive linguistic and semantic annotations.

Contribution

This paper introduces the CREER dataset, a large-scale, richly annotated corpus for NLP tasks, utilizing Stanford CoreNLP to capture detailed language structures from Wikipedia.

Findings

Enables improved NLP task performance with rich annotations

Supports multiple NLP tasks due to comprehensive linguistic features

Provides a publicly accessible large-scale dataset

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. We publicize the dataset through the link: https://140.116.82.111/share.cgi?ssid=000dOJ4