Datasets:

GateNLP
/

broad_twitter_corpus

	---
	annotations_creators:
	- crowdsourced
	language_creators:
	- found
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	task_categories:
	- token-classification
	task_ids:
	- named-entity-recognition
	paperswithcode_id: broad-twitter-corpus
	pretty_name: Broad Twitter Corpus
	---

	# Dataset Card for broad_twitter_corpus

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [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)

	## Dataset Description

	- Homepage: [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
	- Repository: [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
	- Paper: [http://www.aclweb.org/anthology/C16-1111](http://www.aclweb.org/anthology/C16-1111)
	- Leaderboard: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
	- Point of Contact: [Leon Derczynski](https://github.com/leondz)

	### Dataset Summary

	This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.

	See the paper, [Broad Twitter Corpus: A Diverse Named Entity Recognition Resource](http://www.aclweb.org/anthology/C16-1111), for details.

	### Supported Tasks and Leaderboards

	* Named Entity Recognition
	* On PWC: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)

	### Languages

	English from UK, US, Australia, Canada, Ireland, New Zealand; `bcp47:en`

	## Dataset Structure

	### Data Instances

	Feature \|Count
	---\|---:
	Documents \|9 551
	Tokens \|165 739
	Person entities \|5 271
	Location entities \|3 114
	Organization entities \|3 732

	### Data Fields

	Each tweet contains an ID, a list of tokens, and a list of NER tags


	- `id`: a `string` feature.
	- `tokens`: a `list` of `strings`
	- `ner_tags`: a `list` of class IDs (`int`s) representing the NER class:

	```
	0: O
	1: B-PER
	2: I-PER
	3: B-ORG
	4: I-ORG
	5: B-LOC
	6: I-LOC
	```

	### Data Splits

	Section\|Region\|Collection period\|Description\|Annotators\|Tweet count
	---\|---\|---\|---\|---\|---:
	A \| UK\| 2012.01\| General collection \|Expert\| 1000
	B \|UK \|2012.01-02 \|Non-directed tweets \|Expert \|2000
	E \|Global\| 2014.07\| Related to MH17 disaster\| Crowd & expert \|200
	F \|Stratified \|2009-2014\| Twitterati \|Crowd & expert \|2000
	G \|Stratified\| 2011-2014\| Mainstream news\| Crowd & expert\| 2351
	H \|Non-UK\| 2014 \|General collection \|Crowd & expert \|2000


	The most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.

	Test: Section F

	Development: Section H (the paper says "second half of Section H" but ordinality could be ambiguous, so it all goes in. Bonne chance)

	Training: everything else


	## Dataset Creation

	### Curation Rationale

	[Needs More Information]

	### Source Data

	#### Initial Data Collection and Normalization

	[Needs More Information]

	#### Who are the source language producers?

	[Needs More Information]

	### Annotations

	#### Annotation process

	[Needs More Information]

	#### Who are the annotators?

	[Needs More Information]

	### Personal and Sensitive Information

	[Needs More Information]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[Needs More Information]

	### Discussion of Biases

	[Needs More Information]

	### Other Known Limitations

	[Needs More Information]

	## Additional Information

	### Dataset Curators

	[Needs More Information]

	### Licensing Information

	Creative Commons Attribution 4.0 International (CC BY 4.0)

	### Citation Information

	```
	@inproceedings{derczynski2016broad,
	title={Broad twitter corpus: A diverse named entity recognition resource},
	author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
	booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
	pages={1169--1179},
	year={2016}
	}
	```

	### Contributions

	Author-added dataset [@leondz](https://github.com/leondz)