In this work, we investigate the application of prompt-learning on fine-grained entity typing in fully supervised, few-shot and zero-shot scenarios.
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers.
In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
Ranked #1 on Named Entity Recognition on Few-NERD (SUP)
We introduce a Poincare probe, a structural probe projecting these embeddings into a Poincare subspace with explicitly defined hierarchies.
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document.
In order to simultaneously alleviate these two issues, this paper proposes to couple distant annotation and adversarial training for cross-domain CWS.
Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy.
In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types.