Cross-Domain Named Entity Recognition
11 papers with code • 1 benchmarks • 1 datasets
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French.
Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets.
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains.
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years.