---
title: "PolyAI-LDN/task-specific-datasets: papers citing it"
description: "4 papers in the tracked journals link to the GitHub repository PolyAI-LDN/task-specific-datasets in their Crossref records, the first in 2024; most of them (2) in 2025."
url: https://scholar4ai.com/code/github-polyai-ldn-task-specific-datasets
markdown_url: https://scholar4ai.com/code/github-polyai-ldn-task-specific-datasets.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the GitHub repository PolyAI-LDN/task-specific-datasets in their Crossref records, the first in 2024; most of them (2) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# PolyAI-LDN/task-specific-datasets: papers linking to it

Research papers in the 480 tracked journals that link to PolyAI-LDN/task-specific-datasets (https://github.com/PolyAI-LDN/task-specific-datasets), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 173 stars
- 57 forks
- Python
- license CC-BY-4.0
- created 2020
- https://github.com/PolyAI-LDN/task-specific-datasets

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the GitHub repository PolyAI-LDN/task-specific-datasets in their Crossref records, the first in 2024; most of them (2) in 2025.
- By category: AI (4); the journals with the most are Applied Intelligence (1), Machine Learning (1) and Knowledge-Based Systems (1).

## By year

*Papers linking to PolyAI-LDN/task-specific-datasets by publication year*

| Year | Papers |
|---|---:|
| 2024 | 2 |
| 2025 | 2 |

## Journals

*Journals whose papers link to PolyAI-LDN/task-specific-datasets most*

| Journal | Papers |
|---|---:|
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |
| [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | 1 |

## Papers

*Papers linking to PolyAI-LDN/task-specific-datasets, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-12-07 | [A comparative analysis of encoder only and decoder only models in intent classification and sentiment analysis: navigating the trade-offs in model size and performance](https://doi.org/10.1007/s10579-024-09796-y) | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | Alberto Benayas, Miguel‐Ángel Sicilia, Marçal Mora‐Cantallops | 16 | cites it |
| 2024-01-01 | [Few-shot out-of-scope intent classification: analyzing the robustness of prompt-based learning](https://doi.org/10.1007/s10489-023-05215-x) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Yiwei Jiang, Maarten De Raedt, Johannes Deleu et al. | 6 | cites it |
| 2025-07-17 | [Seeded Poisson Factorization: leveraging domain knowledge to fit topic models](https://doi.org/10.1016/j.knosys.2025.114116) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Bernd Prostmaier, Jan Vávra, Bettina Grün et al. | 2 | cites it |
| 2025-09-03 | [Linearly-interpretable concept embedding models for text analysis](https://doi.org/10.1007/s10994-025-06839-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Francesco De Santis, Philippe Bich, Gabriele Ciravegna et al. | 1 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). PolyAI-LDN/task-specific-datasets: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-polyai-ldn-task-specific-datasets

```bibtex
@misc{scholar4ai-code-github-polyai-ldn-task-specific-datasets,
  author = {{Scholar4AI}},
  title = {PolyAI-LDN/task-specific-datasets: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-polyai-ldn-task-specific-datasets}},
  urldate = {2026-11-03},
  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/).