petaniindo/wnut_17

---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: wnut-2017-emerging-and-rare-entity
pretty_name: WNUT 17
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-corporation
'2': I-corporation
'3': B-creative-work
'4': I-creative-work
'5': B-group
'6': I-group
'7': B-location
'8': I-location
'9': B-person
'10': I-person
'11': B-product
'12': I-product
config_name: wnut_17
splits:
- name: train
num_bytes: 1078379
num_examples: 3394
- name: validation
num_bytes: 259383
num_examples: 1009
- name: test
num_bytes: 405536
num_examples: 1287
download_size: 800955
dataset_size: 1743298
---

# Dataset Card for "wnut_17"

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** [http://noisy-text.github.io/2017/emerging-rare-entities.html](http://noisy-text.github.io/2017/emerging-rare-entities.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.80 MB
- **Size of the generated dataset:** 1.74 MB
- **Total amount of disk used:** 2.55 MB

### Dataset Summary

WNUT 17: Emerging and Rare entity recognition

This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation),
but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.
Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve.
This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.

The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Dataset Structure

### Data Instances

- **Size of downloaded dataset files:** 0.80 MB
- **Size of the generated dataset:** 1.74 MB
- **Total amount of disk used:** 2.55 MB

An example of 'train' looks as follows.
```
{
"id": "0",
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
"tokens": ["@paulwalk", "It", "'s", "the", "view", "from", "where", "I", "'m", "living", "for", "two", "weeks", ".", "Empire", "State", "Building", "=", "ESB", ".", "Pretty", "bad", "storm", "here", "last", "evening", "."]
}
```

### Data Fields

The data fields are the same among all splits:
- `id` (`string`): ID of the example.
- `tokens` (`list` of `string`): Tokens of the example text.
- `ner_tags` (`list` of class labels): NER tags of the tokens (using IOB2 format), with possible values:
- 0: `O`
- 1: `B-corporation`
- 2: `I-corporation`
- 3: `B-creative-work`
- 4: `I-creative-work`
- 5: `B-group`
- 6: `I-group`
- 7: `B-location`
- 8: `I-location`
- 9: `B-person`
- 10: `I-person`
- 11: `B-product`
- 12: `I-product`

### Data Splits

|train|validation|test|
|----:|---------:|---:|
| 3394| 1009|1287|

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Citation Information

```
@inproceedings{derczynski-etal-2017-results,
title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
author = "Derczynski, Leon and
Nichols, Eric and
van Erp, Marieke and
Limsopatham, Nut",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W17-4418",
doi = "10.18653/v1/W17-4418",
pages = "140--147",
abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization),
but recall on them is a real problem in noisy text - even among annotators.
This drop tends to be due to novel entities and surface forms.
Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'}
hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities,
and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the
ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}

```

### Contributions

Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@stefan-it](https://github.com/stefan-it), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu) for adding this dataset.

WNUT 17: Emerging and Rare entity recognition. This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet so.. kktny in 30 mins? - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.

搜集汇总

数据集介绍

构建方式

WNUT 17数据集源自第三届噪声用户生成文本研讨会（WNUT 2017）的共享任务，旨在聚焦新兴与罕见实体的识别挑战。该数据集通过众包方式收集自社交媒体等噪声文本，由标注人员采用IOB2格式对命名实体进行标注，涵盖公司、创意作品、团体、地点、人物和产品六类实体。数据集包含训练集3394例、验证集1009例和测试集1287例，总计约5690个样本，其构建过程强调对先前未见过实体及其表面形式的捕捉，以模拟真实场景下实体识别的困难。

使用方法

研究者可直接通过HuggingFace datasets库加载WNUT 17数据集，使用命令'load_dataset("wnut_17")'即可获取预划分的训练、验证和测试集。数据输入为'id'、'tokens'和'ner_tags'字段，其中'ner_tags'为整数序列，对应实体的BIO标签。该数据集适用于基于Transformer的序列标注模型（如BERT、RoBERTa）的微调与评估，在模型输出层需配置对应的六类实体识别的分类头，以完成命名实体识别任务。

背景与挑战

背景概述

WNUT 17（Workshop on Noisy User-generated Text 2017）数据集由Leon Derczynski、Eric Nichols、Marieke van Erp和Nut Limsopatham等研究者于2017年构建，旨在应对新兴与稀有实体识别这一自然语言处理领域的核心挑战。该数据集聚焦于从社交媒体等噪声文本中检测和分类前所未见、新兴出现的命名实体，如推文中出现的模糊指代“kktny”。作为第三届噪声用户生成文本研讨会（WNUT）的共享任务，该数据集为评估模型在低资源、高噪声场景下的实体识别能力提供了标准化基准，推动了命名实体识别（NER）研究向更贴近现实、动态变化的文本环境迈进，对事件聚类、文本摘要等下游任务产生了重要影响。

当前挑战

WNUT 17数据集所解决的领域问题在于，传统命名实体识别模型在面对新兴、稀有或未见过的实体时表现不佳，尤其是在用户生成文本中的拼写变异、简称和口语化表达导致实体边界模糊、类别不明。构建过程中面临的核心挑战包括：如何从社交媒体语料中有效筛选并标注那些缺乏先验知识的罕见实体，以及确保标注者之间对新兴实体（如“kktny”）的判断具有一致性。此外，数据规模有限（训练集仅3394例），使得模型在捕捉实体分布的长尾特征时面临过拟合与泛化能力不足的双重困境。

常用场景

经典使用场景

WNUT 17 数据集专为新兴与稀有实体识别任务而生，聚焦于嘈杂文本（如社交媒体推文）中那些前所未见、涌现出的命名实体。其经典使用场景在于评估和提升命名实体识别（NER）模型在非规范、高噪声语境下的鲁棒性，尤其针对那些因拼写变异、缩写或新词涌现而导致传统NER系统召回率骤降的挑战性实例。例如，识别推文中如“kktny”这类人类专家都难以捉摸的罕见实体，正是该数据集设计的初衷。

解决学术问题

该数据集精准地解决了学术研究中一个长期被忽视的难题：在噪声文本中识别新兴与罕见实体。传统NER模型过度依赖静态词汇表和上下文模式，面对不断涌现的新奇实体及其表面形式时，性能显著退化。WNUT 17 为此提供了一套标准化的评估基准，推动了研究者探索更鲁棒的上下文表征、跨领域泛化策略以及小样本学习范式，深刻影响了自然语言处理领域对开放世界实体识别问题的研究范式。

实际应用

在实际应用层面，WNUT 17 所关注的实体识别能力对社交媒体监控、突发事件追踪、舆情分析及品牌声誉管理等场景具有极高价值。例如，在实时监测Twitter上关于新兴产品的讨论时，系统需要快速识别出尚未收录于知识库中的品牌名或代号；在公共安全领域，及时捕捉非规范表达的场所或人物信息亦依赖于此类技术。该数据集催生的模型可无缝嵌入信息抽取管线，为下游任务提供更全面的实体线索。

数据集最近研究

最新研究方向

在自然语言处理的前沿阵地上，WNUT 17数据集聚焦于嘈杂用户生成文本中新兴与罕见实体的识别挑战，这一研究方向与社交媒体爆炸式增长的热点事件紧密相连。随着Twitter等平台成为信息传播的主战场，诸如突发新闻、病毒式传播中的全新组织名、人物名或产品名等实体频繁涌现，传统命名实体识别模型在应对这些未见过的表面形式时往往捉襟见肘。该数据集的问世为攻克这一痛点提供了基准，推动学界探索基于上下文提示、跨模态融合或小样本学习的创新方法，其意义在于提升信息抽取系统在真实动态环境下的鲁棒性与时效性，进而赋能舆情监测、事件追踪等下游任务的精准执行。