# Method and Dataset Mining in Scientific Papers

arXiv: https://arxiv.org/abs/1911.13096 · HTML: https://mlsanity.com/paper/1911.13096/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [DMDD: A Large-Scale Dataset for Dataset Mentions Detection](https://mlsanity.com/paper/2305.11779/) (2023)
- [SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions](https://mlsanity.com/paper/2406.14756/) (2024)
- [Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles](https://mlsanity.com/paper/2109.00199/) (2021)

## Abstract (arXiv)

Literature analysis facilitates researchers better understanding the development of science and technology. The conventional literature analysis focuses on the topics, authors, abstracts, keywords, references, etc., and rarely pays attention to the content of papers. In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers. The extraction and mining of M and D are useful for discipline analysis and algorithm recommendation. In this paper, we propose a novel entity recognition model, called MDER, and constructe datasets from the papers of the PAKDD conferences (2009-2019). Some preliminary experiments are conducted to assess the extraction performance and the mining results are visualized.