Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
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. |