Papers with label interpretation learning phase

1 papers
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)

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Challenge: Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text.
Approach: They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition.
Outcome: The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations.

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