REVIEW 1 cited by
CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization, and location, it contains more diverse categories. It is more challenging than current other Chinese NER datasets and could better reflect real-world applications. For comparison, we implement several state-of-the-art baselines as sequence labeling tasks and report human performance, as well as its analysis. To facilitate future work on fine-grained NER for Chinese, we release our dataset, baselines, and leader-board.
Forward citations
Cited by 1 Pith paper
-
Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information
A PVI-based data reduction and progressive training strategy is applied to Chinese NLI, but the reported small accuracy declines do not match the experimental tables.
Discussion (0). Continue with ORCID to comment.