README.md

---
language:
- en
license: cc-by-4.0
pretty_name: EMNLP 2020-2025 Atomic Contribution Claims (ACC) with Drift Clusters
tags:
- synthetic
- scientometrics
- science-of-science
- acl-anthology
- emnlp
- atomic-claims
- claim-extraction
- clustering
- scientific-text
- nlp
annotations_creators:
- machine-generated
language_creators:
- machine-generated
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- feature-extraction
- text-classification
configs:
- config_name: claims
  data_files:
  - split: train
    path: claims.parquet
  default: true
- config_name: papers
  data_files:
  - split: train
    path: papers.parquet
- config_name: clusters
  data_files:
  - split: train
    path: clusters.parquet
- config_name: cluster_summary
  data_files:
  - split: train
    path: cluster_summary.parquet
---

# 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](https://aclanthology.org/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](https://huggingface.co/datasets/Hamyrappy/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%.

```python
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:

```bibtex
@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}}
}
```