Papers › Domain-independent Extraction of Scientific Concepts from Research Articles
Domain-independent Extraction of Scientific Concepts from Research Articles
Arthur Brack, Jennifer D'Souza, Anett Hoppe, Sören Auer, Ralph Ewerth
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scientific Concept Extraction | STM-corpus | SciBERT (active learning) | Exact Span F1 | 66.4 | #1 of 2 | Archive leaderboard | report |
| Scientific Concept Extraction | STM-corpus | SciBERT (full data) | Exact Span F1 | 65.5 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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