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TweetNERD / README.md
Shubhanshu Mishra
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metadata
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

Dataset DOI arXiv Poster Slides YouTube Video Views

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) 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 (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, which requires you to agree to the Developer Terms Policies and Agreements.

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}
}