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meihant
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DocEE

Tasks:
Text Classification
Token Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
event-extraction
document-level-event-extraction
docee
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---
language:
- en
pretty_name: DocEE English — Normal Setting (Schema Complete)
task_categories:
- text-classification
- token-classification
size_categories:
- 10K<n<100K
tags:
- event-extraction
- document-level-event-extraction
- docee
configs:
- config_name: normal
default: true
data_files:
- split: train
path: data/train-*.jsonl
- split: validation
path: data/validation-*.jsonl
- split: test
path: data/test-*.jsonl
---
# DocEE: Document-level Event Extraction
## Dataset overview
**DocEE** is a benchmark for extracting events and their arguments from full news documents, introduced by Tong et al. at NAACL 2022. Its annotations describe the main event in each document and the people, organizations, locations, times, and other event-specific information associated with it. Arguments can occur across multiple sentences, making the dataset suitable for studying document-level understanding. See the [paper](https://aclanthology.org/2022.naacl-main.291/) for the dataset construction process and original experiments.
The English ontology contains **59 event types** and **356 distinct argument types**. The event types comprise **31 hard-news types** and **28 soft-news types**, with a dedicated argument schema for each event type.
This dataset reformats the official English [DocEE dataset](https://github.com/tongmeihan1995/DocEE), using its **normal_setting** train/dev/test files. It contains one main event per document and preserves the original split membership and retained row order. The upstream `dev` split is named `validation` here. The cross-domain setting is not included.
**Chinese counterpart:** [meihant/DocEE-zh](https://huggingface.co/datasets/meihant/DocEE-zh) provides Chinese news event extraction data, based on [Liu et al., Findings of EMNLP 2024](https://aclanthology.org/2024.findings-emnlp.35/). It contains **34,536 records**, **58 event types**, and **344 argument types**, with full data and reproducible train/validation/test splits. Both datasets use the same four-field format.
This Hugging Face release provides **27,478 documents**, with all roles from the corresponding event schema included in every record.
## How the event schema is designed
DocEE draws on the distinction between **hard news** and **soft news** in journalism:
| Event group | Number of event types | Focus | Examples |
|---|---:|---|---|
| Hard news | 31 | Urgent events with immediate public consequences | Earthquakes, road crashes, armed conflicts |
| Soft news | 28 | Human-interest, celebrity, sports, and entertainment stories | Celebrity marriages, sports competitions, awards ceremonies |
These are broad groups used to design the ontology; each document receives one of the **59 specific event labels**. See the [original repository](https://github.com/tongmeihan1995/DocEE) and [paper, Section 3.1](https://aclanthology.org/2022.naacl-main.291.pdf).
For each event type, the authors first examined **20 Wikipedia event pages** and used shared infobox keys as candidate argument roles. They then collected **20 New York Times stories** of that type. **Five journalism students** identified additional facts readers would want to know; their suggestions were merged, trial annotation was conducted, and rarely occurring roles were filtered out. This produces both common roles, such as dates, and event-specific roles, such as earthquake magnitude or flood water level. [Schema construction details](https://aclanthology.org/2022.naacl-main.291.pdf).
An event schema therefore specifies **which kinds of information to extract for a given event type**. The ontology has 356 distinct role names overall; each event uses only its own subset. For example, the released `Earthquakes` schema has **15 roles**, including `Epicenter`, `Magnitude`, `Affected Area`, and `Casualties and Losses`. The complete role lists are available in [schemas.json](schemas.json).
## Supported tasks
- **Event classification:** predict the main event type from the event trigger and document.
- **Event argument extraction:** extract the annotated text values for each role in an event schema.
- **Joint event extraction:** predict the event type and its arguments together.
The `event_type` field and `arguments` are gold annotations. For event classification or joint extraction, use the event trigger and document as input and these annotations as supervision. The event ontology is available separately in `schemas.json`.
## Dataset statistics
| Property | Value |
|---|---:|
| Language | English |
| Setting | Normal |
| Documents / main events | 27,478 |
| Event types | 59 (31 hard news + 28 soft news) |
| Argument types (distinct role names) | 356 |
| Argument text values | 228,692 |
Argument values are counted once per distinct text within each document and role.
| Split | Documents | Argument text values |
|---|---:|---:|
| Train | 21,960 | 190,511 |
| Validation | 2,748 | 19,000 |
| Test | 2,770 | 19,181 |
| **Total** | **27,478** | **228,692** |
The official normal-setting split membership is retained; seven malformed records were excluded.
## Fields
Exactly four fields are provided per record:
| Field | Type | Meaning |
|---|---|---|
| `event_trigger` | string | Original news title, used as event-trigger context (not an annotated trigger span). |
| `document` | string | Original full document, unchanged. |
| `event_type` | string | Original event classification label. |
| `arguments` | list of objects | One `{role, values}` entry for every schema role. `values` is a list of all annotated texts for that role, or JSON `null` if unannotated. |
Every schema role appears exactly once. Unannotated roles have JSON `null` values, which become Python `None`. Annotated roles contain a list of distinct text values in first-occurrence order: identical strings within the same document and role are included only once. `schemas.json` provides the complete event ontology.
## Example: an earthquake event
The following is a complete record from the **test split**, about the 1933 Diexi earthquake. Its document text and annotations are reproduced as stored in this release. It demonstrates the four fields and **all 15 roles** in the `Earthquakes` schema.
```json
{
"event_trigger": "1933 Diexi earthquake",
"document": "The 1933 Diexi earthquake occurred in Diexi, Mao County, Szechwan, Republic of China on August 25 with a moment magnitude of 7.3 and a maximum Mercalli intensity of X (Extreme). With up to 9,300 killed, this was the deadliest earthquake of 1933. This earthquake destroyed the town of Diexi and surrounding villages, and caused many landslides, and killed about 9,000 people. [4] The old town of Diexi sank into the landslide dam-created Diexi Lake. ",
"event_type": "Earthquakes",
"arguments": [
{"role": "Number of Rebuilding House", "values": null},
{"role": "Number of Destroyed Building", "values": null},
{"role": "Economic Loss", "values": null},
{"role": "Epicenter", "values": null},
{"role": "Number of Trapped People", "values": null},
{"role": "Number of Aftershocks", "values": null},
{"role": "Aid Agency", "values": null},
{"role": "Number of Evacuated People", "values": null},
{"role": "Casualties and Losses", "values": ["up to 9,300 killed"]},
{"role": "Affected Area", "values": ["Diexi, Mao County, Szechwan, Republic of China"]},
{"role": "Temporary Settlement", "values": null},
{"role": "Date", "values": ["1933", "August 25"]},
{"role": "Number of Rescued People", "values": null},
{"role": "Magnitude", "values": ["7.3"]},
{"role": "Aid Supplies/Amount", "values": null}
]
}
```
Here, `event_trigger` is the source title and `event_type` is the specific class `Earthquakes`, a hard-news event type. `Magnitude` has one annotated value, while `Date` has two. Roles without an annotation, such as `Economic Loss`, remain present with `values: null` (Python `None`). **Null means that this role has no gold annotation; it does not establish that the corresponding fact is absent in the real world.** Values are distinct text strings within each document and role.
The record does not contain a separate `schema` field: the role names are listed in `arguments`, and the reusable ontology is provided in `schemas.json`.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("meihant/DocEE", "normal")
row = ds["train"][0]
arguments_by_role = {entry["role"]: entry["values"] for entry in row["arguments"]}
# arguments_by_role[role] is None or a list of all annotated strings.
```
The JSONL files can also be read directly with Python's standard `json` module. For local use with `datasets`, supply the three split-specific globs under `data_files` to `load_dataset("json", data_files=...)`.
For example, an unannotated role is represented as `{"role": "Date", "values": null}` in JSON. An annotated role is represented as `{"role": "Date", "values": ["Friday evening", "Saturday"]}`. These are format illustrations rather than a dataset record.
## Resources
- [Original dataset repository](https://github.com/tongmeihan1995/DocEE)
- [Paper (NAACL 2022)](https://aclanthology.org/2022.naacl-main.291/)
- [Official normal-setting data](https://drive.google.com/drive/folders/1fUuFM0Dm59KUKA1EFP3rk8GONJU1s-ud)
- [Event schemas](schemas.json)
Conversion code, source information, and detailed validation statistics are included in `scripts/prepare_dataset.py`, `source_manifest.json`, and `validation_report.json`. The source split membership, including document overlap, is preserved.
## License
No explicit data license was provided in the inspected upstream repository or download files. This reformat does not assign a new license; original dataset and article rights remain with their respective owners.
## Citation
If you use this dataset, please cite the original DocEE paper:
MeiHan Tong, Bin Xu, Shuai Wang, Meihuan Han, Yixin Cao, Jiangqi Zhu, Siyu Chen, Lei Hou, and Juanzi Li. 2022. **DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction.** In *Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*, pages 3970–3982, Seattle, United States. Association for Computational Linguistics. [Paper and citation](https://aclanthology.org/2022.naacl-main.291/). DOI: [10.18653/v1/2022.naacl-main.291](https://doi.org/10.18653/v1/2022.naacl-main.291).
```bibtex
@inproceedings{tong-etal-2022-docee,
title = "{D}oc{EE}: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction",
author = "Tong, MeiHan and Xu, Bin and Wang, Shuai and Han, Meihuan and Cao, Yixin and Zhu, Jiangqi and Chen, Siyu and Hou, Lei and Li, Juanzi",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.291/",
doi = "10.18653/v1/2022.naacl-main.291",
pages = "3970--3982"
}
```
To identify this particular preprocessing, also reference [meihant/DocEE](https://huggingface.co/datasets/meihant/DocEE) and record the dataset revision used in your experiments.