Hamyrappy/emnlp-2020-2025-atomic-claims

EMNLP 2020–2025 Atomic Contribution Claims (ACC), with drift clusters

18,293 atomic contribution claims extracted from the abstracts of the full EMNLP main track 2020–2025, plus the canonical 80-cluster drift clustering and per-cluster drift statistics used in the Drift Inspector paper.

An atomic contribution claim (ACC) is a single self-contained sentence stating one concrete contribution of a paper: atomic (one contribution-bearing proposition), decontextualized (pronouns resolved, meta-language removed), and falsifiable (a verifiable assertion).

TheoremLlama uses curriculum learning and block training techniques to train large language models for formal theorem proving. — from 2024.emnlp-main.667

This is the single-venue case-study corpus of the Drift Inspector project — the corpus every number in the paper's evaluation is computed on. For breadth, see the full-anthology release (346k claims, 423 venues):

• 🤗 Full-anthology dataset: acl-anthology-atomic-claims
• 🔭 *Live demo (extended, 6 ACL venues): https://hamyrappy.github.io/drift-inspector-acl/
• 📄 This corpus explored interactively: https://hamyrappy.github.io/drift-inspector-emnlp/
• 🛠 Code (extraction + clustering + inspector): https://github.com/Hamyrappy/drift-inspector

What's inside

Config	Rows	Contents
claims (default)	18,293	one row per extracted claim (4,937 papers yielded claims)
papers	6,517	all EMNLP main-track papers 2020–2025, with abstracts
clusters	16,576	the paper's canonical clustering: claim → cluster, on a year-balanced subsample
cluster_summary	81	per-cluster names, sizes, per-year document frequency, drift statistics

claims fields

Column	Type	Description
claim_id	string	unique id (clm_ + hash)
paper_id	string	ACL Anthology id, e.g. 2021.emnlp-main.602
year	int32	2020–2025
atomic_claim	string	the claim text
claim_index	int32	position within the paper's extraction (0-based)
extractor_model	string	qwen/qwen3-235b-a22b-thinking-2507
prompt_hash	string	f9a9b434c72c — exact prompt ships with the code
run_id, created_at	string	extraction provenance

papers fields

paper_id, year, title, venue (EMNLP), track (main), url, authors, author_ids, n_authors, abstract (all abstracts are 2020+, hence CC BY 4.0).

clusters fields — the canonical drift clustering

The paper's analysis runs on a year-balanced subsample: 748 papers per year (seed 42) → 4,488 papers, 16,576 claims. Claims were embedded with SPECTER2, reduced with UMAP, clustered with HDBSCAN (BERTopic-style, but the input unit is the claim, not the abstract) → 80 clusters + noise (36% of claims; rising 31% → 42% across years as the newest contributions have had the least time to consolidate).

Column	Type	Description
claim_id	string	joins to claims
paper_id, year, atomic_claim		copied for standalone use
cluster	int32	cluster id; -1 = noise
cluster_descriptor	string	extractive c-TF-IDF descriptor, e.g. attention, parameters, transformer
cluster_short_name	string	LLM-generated, author-reviewed name, e.g. Transformers & Efficiency
cluster_full_name	string	longer readable name

cluster_summary fields

One row per cluster (incl. noise): cluster, cluster_short_name, cluster_full_name, cluster_descriptor, cluster_description (one-paragraph scope note), tags, n_claims, n_papers, df_pct_2020 … df_pct_2025 (share of that year's 748 papers with ≥1 claim in the cluster, %), drift_log2_2025_2020 (log2 ratio of 2025 vs 2020 share, ε = 0.1pp — the Drift Inspector's drift color).

Example drift readings: Syntactic Parsing 5.08% → 0.13% of papers; Math & Logic Reasoning 0.94% → 7.62%.

from datasets import load_dataset
clusters = load_dataset("Hamyrappy/emnlp-2020-2025-atomic-claims", "clusters", split="train")

Dataset creation & validation

1. Corpus. Full EMNLP main track 2020–2025 from the ACL Anthology (751 / 847 / 826 / 1,047 / 1,237 / 1,809 papers per year).
2. Extraction. qwen3-235b-a22b-thinking-2507 (via OpenRouter, temperature 0.2), few-shot prompt enforcing atomicity / decontextualization / falsifiability, excluding background, motivation and raw metric claims. Abstracts with no extractable contribution are excluded (e.g. 3 of 751 in 2020).
3. Human validation. Three annotators labeled 180 items (136 stratified claims + 44 curated negative controls) as Good/Bad/Unsure. Inter-annotator agreement Fleiss' κ = 0.844 (dropping Unsure; 0.76/0.73 under stricter conventions); 97.8/91.8/90.5% Good on sampled claims; 44/44 negatives flagged by 2-of-3 majority. The full protocol and annotations ship with the code repository.
4. LLM judge. An independent judge from a different vendor (Gemini vs. Qwen), blind to human labels, matches the human majority with 94.5% accuracy (κ = 0.863) and flags 43/44 negatives.
5. Clustering. SPECTER2 → UMAP → HDBSCAN on the balanced subsample; 80 clusters, c-TF-IDF descriptors, LLM-generated author-reviewed names. Headline drift directions are stable under bootstrap resampling, eight alternative encoders, and a lexical representation (see paper).

Limitations

• Claims are LLM-extracted; residual errors (unsupported details, background leakage) exist — the validation above quantifies, not eliminates, them.
• Abstract-level only: body-only contributions are invisible.
• Cluster boundaries and names depend on UMAP/HDBSCAN hyperparameters and the encoder; headline drift directions are robustness-checked, per-cluster numbers are descriptive.
• The clustering covers the balanced subsample (16,576 of 18,293 claims); the remaining claims are extracted but unclustered.

Licensing

Released under CC BY 4.0. Claim texts are LLM-generated derived statements; source abstracts (included in papers) are ACL Anthology materials published 2020–2025, licensed CC BY 4.0. Please attribute the ACL Anthology and cite the underlying papers where appropriate.

Citation

The system paper introducing ACCs and Drift Inspector is under review; until then, please cite the dataset directly:

@misc{acc-emnlp-2026,
  title        = {EMNLP 2020--2025 Atomic Contribution Claims (ACC) with Drift Clusters},
  author       = {Karimov, Vsevolod and Ostarkov, Stepan and Poroshina, Anastasia and Frolov, Anatoly and Panchenko, Alexander},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Hamyrappy/emnlp-2020-2025-atomic-claims}}
}