论文标题
CRER：一个大型语料库，用于提取和实体识别
CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition
论文作者
论文摘要
我们描述了CREER数据集的设计和使用，这是一个带有丰富英语语法和语义属性的大型语料库。 CRER数据集使用Stanford Corenlp注释器从Wikipedia纯文本中捕获丰富的语言结构。该数据集遵循广泛使用的语言和语义注释，因此不仅可以用于大多数自然语言处理任务，而且可以用于扩展数据集。这个大型监督数据集可以作为改善未来NLP任务执行的基础。我们通过链接来宣传数据集：https：//140.116.82.111/share.cgi?ssid=000Doj4
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
