SciDef: Automating Definition Extraction from Academic Literature with Large Language Models
Paper
•
2602.05413
•
Published
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DefExtra contains 268 definition records (term, definition, context, type) from 75 papers. We do not ship excerpts from papers due to copyright. Instead, we ship markers and scripts that let users hydrate the dataset from their own PDFs.
Why this workflow:
| Source | Concept | Definition | Context (excerpt) |
|---|---|---|---|
| https://aclanthology.org/2024.lrec-main.952 | media bias |
“a skewed portrayal of information favoring certain group interests, which manifests in multiple facets, including political, gender, racial, and linguistic biases.” | “Media bias is a skewed portrayal of information favoring certain group interests … Such subtypes of bias … make the classification of media bias a challenging task.” |
| https://arxiv.org/abs/2312.16148 | spin bias |
“a form of bias introduced either by leaving out necessary information or by adding unnecessary information.” | “Spin Bias describes a form of bias introduced either by leaving out necessary information … or by adding unnecessary information.” |
pdfs/ (filename should match paper_id, DOI/PII alias, or arXiv ID).docs/defextra_hydration.md).uv run python scripts/hydrate_defextra.py \
--legal-csv data/defextra_legal.csv \
--pdf-dir pdfs \
--grobid-out grobid_out \
--output-csv defextra_hydrated.csv \
--report defextra_hydrated_report.txt \
--require-complete
docs/get_pdfs.md for sources and a helper script that lists required PDFs.defextra_required_pdfs.csv and defextra_required_pdfs.md are precomputed lists.pyproject.toml with all dependencies.uv run python ... and uv will resolve/install deps.data/defextra_legal.csv / data/defextra_legal.parquet: DefExtra markers (no excerpts).The hydrated output (e.g., defextra_hydrated.csv) matches the schema below.
Full legal marker columns are documented in docs/defextra_hydration.md.
| Column | Description |
|---|---|
paper_id |
Paper identifier (often a Semantic Scholar ID, DOI, or arXiv ID). |
paper_title |
Paper title. |
paper_doi |
DOI (if available). |
paper_arxiv |
arXiv ID or URL (if available). |
concept |
Term / concept being defined. |
definition |
Definition text (hydrated from PDFs). |
context |
Context excerpt (hydrated from PDFs). |
definition_type |
Definition type (e.g., explicit / implicit). |
source_file |
Source JSON filename used during curation. |
is_out_of_domain |
Boolean flag for out‑of‑domain papers. |
scripts/hydrate_defextra.py: hydrate DefExtra from PDFs + GROBID.scripts/pdf_to_grobid.py: batch GROBID runner (requires a running GROBID server).scripts/list_defextra_pdfs.py: list required PDFs + download links.scripts/build_defextra_test_pdfs.py: build a test PDF set from a larger PDF pool.scripts/report_defextra_status.py: summarize missing items by paper/definition.docs/defextra_hydration.md (technical details, CLI flags, markers).docs/get_pdfs.md (how to find PDFs).docs/mismatch_examples.md (mismatch types with short excerpts).--allow-pdf-hash-mismatch only after manual inspection.@misc{kucera2026scidefautomatingdefinitionextraction,
title={SciDef: Automating Definition Extraction from Academic Literature with Large Language Models},
author={Filip Ku\v{c}era and Christoph Mandl and Isao Echizen and Radu Timofte and Timo Spinde},
year={2026},
eprint={2602.05413},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2602.05413},
}