Datasets:
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
(you can use
convert_to_parquet
from the datasets library). 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
- 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: astringfeature.tokens: alistofstringsner_tags: alistof 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
- 65