# Dataset Mention Extraction in Scientific Articles Using Bi-LSTM-CRF Model

arXiv: https://arxiv.org/abs/2405.13135 · HTML: https://mlsanity.com/paper/2405.13135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 5 of 5)

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Exploring the Limits of Language Modeling](https://mlsanity.com/paper/1602.02410/) (2016)
- [Identifying and Improving Dataset References in Social Sciences Full Texts](https://mlsanity.com/paper/1603.01774/) (2016)
- [A Semi-Automatic Approach for Detecting Dataset References in Social Science Texts](https://mlsanity.com/paper/1611.01820/) (2016)
- [Assigning credit to scientific datasets using article citation networks](https://mlsanity.com/paper/2001.05917/) (2020)

## Abstract (arXiv)

Datasets are critical for scientific research, playing an important role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent efforts by data repositories and funding agencies. This greatly affects our ability to track their usage and importance. A potential solution to this problem is to automatically extract dataset mentions from scientific articles. In this work, we propose to achieve such extraction by using a neural network based on a Bi-LSTM-CRF architecture. Our method achieves F1 = 0.885 in social science articles released as part of the Rich Context Dataset. We discuss the limitations of the current datasets and propose modifications to the model to be done in the future.