Low Resource Named Entity Recognition

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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

5 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

15 shown of 15 papers with code (38 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.

• Towards Robust Named Entity Recognition for Historic German
  18 Jun 2019 2 repositories listed
  Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference.
• GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
  5 Oct 2023 1 repository listed Syntology ran 18 of 25 samples · 7 unverified
  In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines.
• Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
  23 May 2023 1 repository listed
  Large language models (LLMs) combined with instruction tuning have shown significant progress in information extraction (IE) tasks, exhibiting strong generalization capabilities to unseen datasets by following…
• SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
  11 Oct 2022 1 repository listed
  Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used.
• A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
  11 Apr 2022 1 repository listed
  Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain…
• InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
  8 Mar 2022 1 repository listed
  Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
• Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger
  15 Sep 2021 1 repository listed
  This paper presents a simple and effective approach in low-resource named entity recognition (NER) based on multi-hop dependency trigger.
• Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
  26 Aug 2021 1 repository listed
  The state of art natural language processing systems relies on sizable training datasets to achieve high performance.
• Memorisation versus Generalisation in Pre-trained Language Models
  16 Apr 2021 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data.
• ANEA: Distant Supervision for Low-Resource Named Entity Recognition
  25 Feb 2021 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists.
• A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
  2 Jan 2021 1 repository listed
  Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data.
• Soft Gazetteers for Low-Resource Named Entity Recognition
  4 May 2020 1 repository listed
  However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages.
• Zero-Resource Cross-Lingual Named Entity Recognition
  22 Nov 2019 1 repository listed
  Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features.
• Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels
  14 Oct 2019 1 repository listed
  In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy.
• Massively Multilingual Transfer for NER
  1 Feb 2019 1 repository listed
  In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language.

Syntology lines on 3 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.