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

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

← Code and data repositories cited in research papers

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.

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#

20242025

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	1
Machine Learning	1
Knowledge-Based Systems	1
Language Resources and Evaluation	1

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	Language Resources and Evaluation	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	Applied Intelligence	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	Knowledge-Based Systems	Bernd Prostmaier, Jan Vávra, Bettina Grün et al.	2	cites it
2025-09-03	Linearly-interpretable concept embedding models for text analysis	Machine Learning	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

@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}
}

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