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
Latest papers
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.
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.
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.
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.
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.
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.
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.
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.
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.
