README.md

metadata

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 Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information

Dataset Description

• Homepage: https://github.com/GateNLP/broad_twitter_corpus
• Repository: https://github.com/GateNLP/broad_twitter_corpus
• Paper: http://www.aclweb.org/anthology/C16-1111
• Leaderboard: Named Entity Recognition on Broad Twitter Corpus
• Point of Contact: Leon Derczynski

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, for details.

Supported Tasks and Leaderboards

• Named Entity Recognition
• On PWC: Named Entity Recognition on Broad Twitter Corpus

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 (ints) 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