---
title: "CoNLL: tasks, downloads and usage conditions"
description: "CoNLL: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/conll
markdown_url: https://scholar4ai.com/datasets/conll.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "CoNLL: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# CoNLL

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/2.md)

Corpora and benchmarks for language understanding, question answering and text classification.

- data category: Text and NLP benchmarks
- 0 papers in the last 7 days
- Names searched for: CoNLL

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Text and NLP benchmarks datasets: https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*NLP dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| CoNLL-2003 NER | Token-level named-entity recognition: person, organization, location and miscellaneous. Evaluation: Entity-span F1 using the prescribed evaluation; token accuracy is not an equivalent metric. | [Official source / documentation](https://aclanthology.org/W03-0419/) [Source-text access](https://trec.nist.gov/data/reuters/reuters.html) The shared-task paper documents the annotation format. Obtain source Reuters text through NIST and follow the task preparation instructions; the legacy task website may be unavailable. | Shared-task annotations and source news text have distinct conditions; English Reuters text access follows the applicable corpus agreement. |

Guide updated 2026-11-04.

## Key findings

- From Nov 2023 to Oct 2026, 118 archived papers mentioned CoNLL in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jul 2024 (7).
- No paper of the tracked journals mentioned CoNLL in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), CoNLL was mentioned in 127 papers in the tracked journals, most of them in AI (109), higher education and research (7) and operations research (4).
- 37 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Centre National de la Recherche Scientifique (5), Jožef Stefan Institute (5) and Jožef Stefan International Postgraduate School (5).
- The journals publishing most of them were Language Resources and Evaluation (20), Artificial Intelligence Review (16) and Knowledge-Based Systems (9).
- The most-cited of them is “A survey on few-shot class-incremental learning” (Neural Networks, 2023), with 221 citations.
- Papers use CoNLL most often with Wikipedia (49), Hugging Face Hub (35) and Twitter / X (35); it has 24 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 45 | 41 | 32 | 118 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 2 | 0 | 1 | 2 | 8 | 42 | 31 | 23 |
| [Higher Education and Research](https://scholar4ai.com/categories/higher-education-and-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 2 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| [Statistics](https://scholar4ai.com/categories/statistics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 0 | 0 | 2 | 0 | 1 | 2 | 8 | 50 | 37 | 27 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | 20 |
| 2 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 16 |
| 3 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 9 |
| 4 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 7 |
| 5 | [Computer Speech & Language](https://scholar4ai.com/journals/computer-speech-language.md) | 6 |
| 6 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 6 |
| 7 | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 6 |
| 8 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 5 |
| 9 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 5 |
| 10 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 5 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 5 |
| 2 | Jožef Stefan Institute | 5 |
| 3 | Jožef Stefan International Postgraduate School | 5 |
| 4 | [University of Edinburgh](https://scholar4ai.com/institutions/university-of-edinburgh.md) | 5 |
| 5 | [KU Leuven](https://scholar4ai.com/institutions/ku-leuven.md) | 4 |
| 6 | Charles University | 3 |
| 7 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 3 |
| 8 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 3 |
| 9 | [Queen Mary University of London](https://scholar4ai.com/institutions/queen-mary-university-of-london.md) | 3 |
| 10 | [University of Bologna](https://scholar4ai.com/institutions/university-of-bologna.md) | 3 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 30 |
| 2 | United Kingdom | 21 |
| 3 | Spain | 14 |
| 4 | Italy | 11 |
| 5 | France | 10 |
| 6 | United States | 9 |
| 7 | Australia | 8 |
| 8 | Germany | 8 |
| 9 | Canada | 7 |
| 10 | Singapore | 6 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 29 |
| 2 | named entity recognition | 20 |
| 3 | [BERT](https://scholar4ai.com/keywords/bert.md) | 8 |
| 4 | pre-trained language models | 7 |
| 5 | cross-lingual transfer | 5 |
| 6 | [few-shot learning](https://scholar4ai.com/keywords/few-shot-learning.md) | 5 |
| 7 | [knowledge graphs](https://scholar4ai.com/keywords/knowledge-graphs.md) | 5 |
| 8 | low-resource languages | 5 |
| 9 | natural language processing | 5 |
| 10 | [self-supervised learning](https://scholar4ai.com/keywords/self-supervised-learning.md) | 5 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 221 | 6.1 | [A survey on few-shot class-incremental learning](https://doi.org/10.1016/j.neunet.2023.10.039) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 2023-10-31 | Songsong Tian, Lusi Li, Weijun Li et al. |
| 2 | 166 | 5.8 | [A survey of safety and trustworthiness of large language models through the lens of verification and validation](https://doi.org/10.1007/s10462-024-10824-0) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-06-17 | Xiaowei Huang, Wenjie Ruan, Wei Huang et al. |
| 3 | 145 | 4.4 | [Survey of transformers and towards ensemble learning using transformers for natural language processing](https://doi.org/10.1186/s40537-023-00842-0) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 2024-02-04 | Hongzhi Zhang, M. Omair Shafiq |
| 4 | 110 | 4.1 | [Self-Supervised Multimodal Learning: A Survey](https://doi.org/10.1109/tpami.2024.3429301) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-08-07 | Yongshuo Zong, Oisin Mac Aodha, Timothy M. Hospedales |
| 5 | 56 | 2.3 | [Evaluation of LLM-based chatbots for OSINT-based Cyber Threat Awareness](https://doi.org/10.1016/j.eswa.2024.125509) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2024-10-11 | Samaneh Shafee, Alysson Bessani, Pedro Ferreira |

## Used together with

Papers use CoNLL most often with Wikipedia (49), Hugging Face Hub (35) and Twitter / X (35); it has 24 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use CoNLL together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 49 | 18.2 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 35 | 28.2 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 35 | 3.0 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 11 | 1.4 |
| [GLUE / SuperGLUE](https://scholar4ai.com/datasets/glue-superglue.md) | Dataset | 11 | 38.4 |
| [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md) | Dataset | 11 | 44.1 |
| [Reddit](https://scholar4ai.com/datasets/reddit.md) | Dataset | 11 | 6.8 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 10 | 1.6 |
| [Pre-training corpora (Common Crawl, C4, The Pile)](https://scholar4ai.com/datasets/pre-training-corpora-common-crawl-c4-the-pile.md) | Dataset | 9 | 30.8 |
| [MMLU](https://scholar4ai.com/datasets/mmlu.md) | Dataset | 8 | 38.1 |
| [Retrieval and QA (MS MARCO, Natural Questions, HotpotQA)](https://scholar4ai.com/datasets/retrieval-and-qa-ms-marco-natural-questions-hotpotqa.md) | Dataset | 8 | 37.7 |
| [Kaggle](https://scholar4ai.com/datasets/kaggle.md) | Dataset | 7 | 4.6 |

The whole network: https://scholar4ai.com/code/network.md

## Cite this page

Scholar4AI. (2026). CoNLL. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/conll

```bibtex
@misc{scholar4ai-datasets-conll,
  author = {{Scholar4AI}},
  title = {CoNLL},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/conll}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).