license: cc-by-nc-sa-4.0
language:
- en
size_categories:
- 100K<n<1M
task_categories:
- question-answering
- text-retrieval
tags:
- scientometrics
- affiliations
- ror
- acl-anthology
- cso
- bibliometrics
configs:
- config_name: papers
data_files: papers/*.parquet
- config_name: affiliations
data_files: affiliations/*.parquet
- config_name: topics
data_files: topics/*.parquet
- config_name: fulltext
data_files: fulltext/*.parquet
- config_name: affiliation_gold
data_files: affiliation_gold/*.parquet
- config_name: benchmark_questions
data_files: benchmark_questions/*.parquet
- config_name: cso_validation
data_files: cso_validation/*.parquet
- config_name: linking_adjudication
data_files: linking_adjudication/*.parquet
- config_name: lora_train
data_files:
- split: train
path: lora_train/train-*.parquet
- split: validation
path: lora_train/validation-*.parquet
ANTHEM-202605 — an Enriched ACL Anthology for Scholarly Comparison
117,386 ACL Anthology papers linked to ROR-normalised author affiliations and Computer Science Ontology (CSO) topic labels, with an evidence span retained for every extraction decision.
Code: https://github.com/MoritzStaudinger/anthem
Configs
| Config | Rows | What |
|---|---|---|
papers |
117,386 | one row per paper; nested fields kept as JSON strings |
affiliations |
516,008 | flat: paper x author x affiliation, with ROR id and evidence |
topics |
1,447,513 | flat: paper x CSO label, source = syntactic | semantic |
fulltext |
117,379 | parsed text per paper: GROBID TEI, PyMuPDF/LLM Markdown, OCR |
affiliation_gold |
781 | hand-annotated affiliation gold set (781 papers) |
benchmark_questions |
99 | comparative-QA benchmark: question, persona, the ROR/topic/year gold specification, and the resolved gold_paper_ids |
cso_validation |
104 | hand-judged CSO topic labels |
linking_adjudication |
805 | hand-verified ROR linking decisions, four strategies side by side |
lora_train |
10,000 | LoRA fine-tuning data for the affiliation adapter (train / validation splits) |
Nested structures are stored as JSON strings because record shape varies across
1952--2025; the flat affiliations and topics configs are the ones to query.
from datasets import load_dataset
aff = load_dataset("Molarix/anthem", "affiliations", split="train")
fulltext is the large config — load it only when you need the text itself.
Licensing
ANTHEM's own layer — the extracted affiliations, ROR links, CSO labels and
evidence spans in papers, affiliations and topics — is released under
CC-BY-NC-SA-4.0.
The license column is provenance, not the release licence: it records the
terms the underlying paper carries in the Anthology. Per the
ACL Anthology copyright policy,
materials prior to 2016 are CC BY-NC-SA 3.0 and materials from 2016 onward are
CC BY 4.0. Papers whose year could not be determined are assigned the more
restrictive pre-2016 term.
9,470 papers were published at third-party (ELRA-published) venues such as LREC. The ACL copyright policy states that it does not cover third-party materials, so these rows carry third_party_venue = true; filter on it if your use needs only ACL-licensed material.
Source PDFs are not redistributed; the Anthology serves those. fulltext
contains parsed renderings (TEI, Markdown, OCR text) produced by this pipeline.
| Underlying licence | Papers |
|---|---|
| CC-BY-4.0 | 69,297 |
| CC-BY-NC-SA-3.0 | 38,618 |
| third-party venue (ELRA-published); see respective organisation | 9,471 |
Construction
Affiliations were extracted from parsed PDFs by an LLM pipeline over combined parser outputs (GROBID TEI + PyMuPDF/LLM Markdown + OCR), then linked to ROR with country context. The model recorded per paper:
provenance_model |
Papers |
|---|---|
| gemini-2.5-flash | 79,666 |
| gemini-2.5-flash-lite | 22,261 |
| gemini-1.5-flash | 15,449 |
| None | 10 |
Personal and sensitive information
The dataset joins author names to institutions and publication years. All of it is already public in the ACL Anthology, and affiliations are as printed at publication time rather than current.
The fulltext config is a faithful parse of the published PDFs, so where a paper printed author e-mail addresses in its header, the TEI and OCR text contain them too. They are not separated into a column, and no contact details beyond what the papers themselves print are added.
Known limitations
Affiliation extraction and linking reach F1 0.760 (hierarchy-aware, parent and child ROR ids treated as equivalent) against a 781-paper decade-stratified gold set; ROR linking alone reaches 0.857 on hand-checked affiliation strings; CSO topic-label precision is 0.713. Roughly 90% of remaining false negatives are linking rather than extraction failures, concentrated in city-level institution clusters and geographically branched corporate entities.
This is a fixed snapshot (ANTHEM-202605); it is not updated in place.
Citation
Please cite the ANTHEM paper; the current reference is kept in the code repository.