CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 27 Apr 2022.
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5 of 15 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2018Deep contextualized word representationscited by 2,290
- 2018Dissecting Contextual Word Embeddings: Architecture and Representationcited by 146
- 2019SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominalscited by 63
- 2018An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocolscited by 18
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Citing papers per year: 2023: 1.
1 paper.