# DMDD: A Large-Scale Dataset for Dataset Mentions Detection

arXiv: https://arxiv.org/abs/2305.11779 · HTML: https://mlsanity.com/paper/2305.11779/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics](https://mlsanity.com/paper/2101.10273/) (2021)
- [Method and Dataset Mining in Scientific Papers](https://mlsanity.com/paper/1911.13096/) (2019)

## Built on by (most cited first, 11 of 11)

- [SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents](https://mlsanity.com/paper/2410.21155/) (2024)
- [ChatPD: An LLM-driven Paper-Dataset Networking System](https://mlsanity.com/paper/2505.22349/) (2025)
- [SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions](https://mlsanity.com/paper/2406.14756/) (2024)
- [SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP](https://mlsanity.com/paper/2509.07801/) (2025)
- [Multi-Disciplinary Dataset Discovery from Citation-Verified Literature Contexts](https://mlsanity.com/paper/2601.05099/) (2026)
- [A Position Paper on the Automatic Generation of Machine Learning Leaderboards](https://mlsanity.com/paper/2505.17465/) (2025)
- [DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04616/) (2025)
- [Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows](https://mlsanity.com/paper/2411.19295/) (2024)
- [Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments](https://mlsanity.com/paper/2502.06551/) (2025)
- [Enriching Social Science Research via Survey Item Linking](https://mlsanity.com/paper/2412.15831/) (2024)
- [Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition](https://mlsanity.com/paper/2606.21890/) (2026)

## Abstract (arXiv)

The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportunities. However, existing corpora for dataset mention detection are limited in size and naming diversity. In this paper, we introduce the Dataset Mentions Detection Dataset (DMDD), the largest publicly available corpus for this task. DMDD consists of the DMDD main corpus, comprising 31,219 scientific articles with over 449,000 dataset mentions weakly annotated in the format of in-text spans, and an evaluation set, which comprises of 450 scientific articles manually annotated for evaluation purposes. We use DMDD to establish baseline performance for dataset mention detection and linking. By analyzing the performance of various models on DMDD, we are able to identify open problems in dataset mention detection. We invite the community to use our dataset as a challenge to develop novel dataset mention detection models.