Cross-Domain Named Entity Recognition

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (19 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• CrossNER: Evaluating Cross-Domain Named Entity Recognition
  8 Dec 2020 5 repositories listed
  Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
• DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition
  1 Oct 2022 1 repository listed
  Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain.
• FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition
  24 Aug 2022 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years.
• A Label-Aware Autoregressive Framework for Cross-Domain NER
  1 Jul 2022 1 repository listed
  In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then…
• DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
  10 Jan 2022 1 repository listed
  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
  4 Sep 2021 1 repository listed
  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
  2 Jul 2021 1 repository listed
  We propose new methods for in-domain and cross-domain Named Entity Recognition (NER) on historical data for Dutch and French.
• Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
  24 Apr 2020 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling.
• Zero-Resource Cross-Domain Named Entity Recognition
  14 Feb 2020 1 repository listed
  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
  1 Jul 2019 1 repository listed
  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
  15 Oct 2018 1 repository listed
  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…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.