# Domain-independent Extraction of Scientific Concepts from Research Articles

arXiv: https://arxiv.org/abs/2001.03067 · HTML: https://mlsanity.com/paper/2001.03067/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

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

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- [Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles](https://mlsanity.com/paper/2109.00199/) (2021)
- [Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models](https://mlsanity.com/paper/2501.18287/) (2025)
- [Easy Semantification of Bioassays](https://mlsanity.com/paper/2111.15182/) (2021)
- [Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers](https://mlsanity.com/paper/2203.01608/) (2022)
- [Expressing High-Level Scientific Claims with Formal Semantics](https://mlsanity.com/paper/2109.12907/) (2021)
- [SciEv: Finding Scientific Evidence Papers for Scientific News](https://mlsanity.com/paper/2205.00126/) (2022)
- [The Digitalization of Bioassays in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14574/) (2022)
- [Overview of STEM Science as Process, Method, Material, and Data Named Entities](https://mlsanity.com/paper/2205.11863/) (2022)

## Abstract (arXiv)

We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of scientific abstracts from 10 domains of Science, Technology and Medicine at the phrasal level in a joint effort with domain experts. The resulting dataset is used in a set of benchmark experiments to (a) provide baseline performance for this task, (b) examine the transferability of concepts between domains. Second, we present two deep learning systems as baselines. In particular, we propose active learning to deal with different domains in our task. The experimental results show that (1) a substantial agreement is achievable by non-experts after consultation with domain experts, (2) the baseline system achieves a fairly high F1 score, (3) active learning enables us to nearly halve the amount of required training data.