Cross-Domain Named Entity Recognition
11 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Cross-Domain Named Entity Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
CoNLL
CrossNER
Most implemented papers
CrossNER: Evaluating Cross-Domain Named Entity Recognition
zliucr/CrossNER
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Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
Neural Adaptation Layers for Cross-domain Named Entity Recognition
yuchenlin/CDMA-NER
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EMNLP 2018
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.
Cross-Domain NER using Cross-Domain Language Modeling
jiachenwestlake/Cross-Domain_NER
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ACL 2019
Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task.
Zero-Resource Cross-Domain Named Entity Recognition
Siddharthss500/zero-resource
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WS 2020
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains.
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
zliucr/coach
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ACL 2020
In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling.
Data Centric Domain Adaptation for Historical Text with OCR Errors
We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French.
Data Augmentation for Cross-Domain Named Entity Recognition
ritual-uh/style_ner
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EMNLP 2021
Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
zjunlp/deepke
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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
lifan-yuan/factmix
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COLING 2022
Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years.
