The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support. If this is not possible, please open a discussion for direct help.

Dataset Card for broad_twitter_corpus

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

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

Downloads last month
91