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---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: "train.public.merged.json"
- split: validation
path: "valid.public.merged.json"
- split: test
path: "test.public.merged.json"
- split: academic
path: "Academic.public.merged.json"
- split: ood
path: "OOD.public.merged.json"
- config_name: paper
data_files:
- split: parts
path:
- "part_*.public.merged.json"
- split: academic
path: "Academic.public.merged.json"
- split: ood
path: "OOD.public.merged.json"
---
# TweetNERD - End to End Entity Linking Benchmark for Tweets
[](https://doi.org/10.5281/zenodo.5013186) [](https://arxiv.org/abs/2210.08129) [](./Neurips_2022_Poster.pdf) [](./Neurips_2022_Slides.pdf) [](https://www.youtube.com/watch?v=H5ypIHterWQ)
This is the *hydrated version* of dataset described in the paper **TweetNERD - End to End Entity Linking Benchmark for Tweets** (to be released soon). It includes the Tweet text based on the Twitter API.
> Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on Tweets. This is the largest and most temporally diverse open sourced dataset benchmark for NERD on Tweets and can be used to facilitate research in this area.
TweetNERD dataset is released under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) LICENSE.
The license only applies to the data files present in this dataset. See **Data usage policy** below.
## Usage
We provide the dataset split across the following tab seperated files:
* **OOD.public.merged.tsv**: OOD split of the data in the paper.
* **Academic.public.merged.tsv**: Academic split of the data described in the paper.
* `part_*.public.merged.tsv`: Remaining data split into parts in no particular order.
Official train test splits:
* `train.public.merged.tsv`: Train split as described in paper based on `part_*` splits.
* `valid.public.merged.tsv`: Validation split as described in paper based on `part_*` splits.
* `test.public.merged.tsv`: Test split as described in paper based on `part_*` splits.
Each file is tab seperated and has has the following format:
| tweet_id | phrase | start | end | entityId | score |
|---------------------|-------------|---------|-------|-------------|---------|
| 22 | [twttr] | [20] | [25] | [Q918] | [3] |
| 21 | [twttr] | [20] | [25] | [Q918] | [3] |
| 1457198399032287235 | [Diwali] | [30] | [38] | [Q10244] | [3] |
| 1232456079247736833 | [NO_PHRASE] | [-1] | [-1] | [NO_ENTITY] | [-1] |
For tweets which don't have any entity, their column values for `phrase, start, end, entityId, score` are set `NO_PHRASE, -1, -1, NO_ENTITY, -1` respectively.
Description of file columns is as follows:
| Column | Type | Missing Value | Description |
|----------|--------|---------------|-------------------------------------------------------------------------------------------------------|
| tweet_id | string | | ID of the Tweet |
| phrase | string | NO_PHRASE | entity phrase |
| start | int | -1 | start offset of the phrase in text using `UTF-16BE` encoding |
| end | int | -1 | end offset of the phrase in the text using `UTF-16BE` encoding |
| entityId | string | NO_ENTITY | Entity ID. If not missing can be NOT FOUND, AMBIGUOUS, or Wikidata ID of format Q{numbers}, e.g. Q918 |
| score | int | -1 | Number of annotators who agreed on the phrase, start, end, entityId information |
In order to use the dataset you need to utilize the `tweet_id` column and get the Tweet text using the [Twitter API](https://developer.twitter.com/en/docs/twitter-api) (See **Data usage policy** section below).
## Data stats
| Split | Number of Rows | Number unique tweets | Number hydrated tweets |
|:---------|-----------------:|-----------------------:|-----------------------:|
| OOD | 34102 | 25000 | 20937 |
| Academic | 51685 | 30119 | 28694 |
| part_0 | 11830 | 10000 | 6633 |
| part_1 | 35681 | 25799 | 19181 |
| part_2 | 34256 | 25000 | 19876 |
| part_3 | 36478 | 25000 | 20611 |
| part_4 | 37518 | 24999 | 20567 |
| part_5 | 36626 | 25000 | 20667 |
| part_6 | 34001 | 24984 | 20948 |
| part_7 | 34125 | 24981 | 20612 |
| part_8 | 32556 | 25000 | 20610 |
| part_9 | 32657 | 25000 | 21000 |
| part_10 | 32442 | 25000 | 20597 |
| part_11 | 32033 | 24972 | 20583 |
|----------|------------------|------------------------|------------------------|
| train | 349252 | 255490 | 207278 |
| valid | 6822 | 5000 | 4128 |
| test | 34129 | 25000 | 20274 |
File Stats are as follows:
| part | output_file | orig_rows | unique_tweet_ids | final_rows |
|----------:|------------------------------:|------------:|------------------:|------------:|
| Academic | Academic.public.merged.json | 51685 | 30119 | 28694 |
| OOD | OOD.public.merged.json | 34102 | 25000 | 20937 |
| part_0 | part_0.public.merged.json | 11830 | 10000 | 6633 |
| part_1 | part_1.public.merged.json | 35681 | 25799 | 19181 |
| part_10 | part_10.public.merged.json | 32442 | 25000 | 20597 |
| part_11 | part_11.public.merged.json | 32033 | 24972 | 20583 |
| part_2 | part_2.public.merged.json | 34256 | 25000 | 19876 |
| part_3 | part_3.public.merged.json | 36478 | 25000 | 20611 |
| part_4 | part_4.public.merged.json | 37518 | 24999 | 20567 |
| part_5 | part_5.public.merged.json | 36626 | 25000 | 20667 |
| part_6 | part_6.public.merged.json | 34001 | 24984 | 20948 |
| part_7 | part_7.public.merged.json | 34125 | 24981 | 20612 |
| part_8 | part_8.public.merged.json | 32556 | 25000 | 20610 |
| part_9 | part_9.public.merged.json | 32657 | 25000 | 21000 |
| test | test.public.merged.json | 34129 | 25000 | 20274 |
| train | train.public.merged.json | 349252 | 255490 | 207278 |
| valid | valid.public.merged.json | 6822 | 5000 | 4128 |
## Data usage policy
Use of this dataset is subject to you obtaining lawful access to the [Twitter API](https://developer.twitter.com/en/docs/twitter-api), which requires you to agree to the [Developer Terms Policies and Agreements](https://developer.twitter.com/en/developer-terms/).
Cite as:
> Mishra, Shubhanshu, Saini, Aman, Makki, Raheleh, Mehta, Sneha, Haghighi, Aria, & Mollahosseini, Ali. (2022). TweetNERD - End to End Entity Linking Benchmark for Tweets (0.0.0) [Data set]. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (Neurips), New Orleans, LA, USA. Zenodo. https://doi.org/10.5281/zenodo.6617192
> Mishra, S., Saini, A., Makki, R., Mehta, S., Haghighi, A., & Mollahosseini, A. (2022). TweetNERD -- End to End Entity Linking Benchmark for Tweets (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2210.08129
Bibtex:
```
@inproceedings{TweetNERD,
doi = {10.48550/ARXIV.2210.08129},
url = {https://arxiv.org/abs/2210.08129},
author = {Mishra, Shubhanshu and Saini, Aman and Makki, Raheleh and Mehta, Sneha and Haghighi, Aria and Mollahosseini, Ali},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Information Retrieval (cs.IR), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7, 68T50, 68T07},
title = {{TweetNERD} -- {End to End Entity Linking Benchmark for Tweets}},
publisher = {arXiv},
year = {2022},
booktitle = "Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 2 (NeurIPS Datasets and Benchmarks 2022)",
copyright = {Creative Commons Attribution 4.0 International}
}
@dataset{mishra_shubhanshu_2022_6617192,
author = {Mishra, Shubhanshu and
Saini, Aman and
Makki, Raheleh and
Mehta, Sneha and
Haghighi, Aria and
Mollahosseini, Ali},
title = {{TweetNERD - End to End Entity Linking Benchmark
for Tweets}},
month = jun,
year = 2022,
note = {{Data usage policy Use of this dataset is subject
to you obtaining lawful access to the [Twitter
API](https://developer.twitter.com/en/docs
/twitter-api), which requires you to agree to the
[Developer Terms Policies and
Agreements](https://developer.twitter.com/en
/developer-terms/).}},
publisher = {Zenodo},
version = {0.0.0},
doi = {10.5281/zenodo.6617192},
url = {https://doi.org/10.5281/zenodo.6617192}
}
``` |