README.md

metadata

language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 1k<10K
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: TweetNER7

Dataset Card for "tner/tweetner7"

Dataset Description

• Repository: https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper
• Paper: TBA
• Dataset: TweetNER7
• Domain: Twitter
• Number of Entity: 7

Dataset Summary

This is an official of TweetNER7 ("Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts, AACL main conference 2022"), an NER dataset on Twitter with 7 entity labels. Each instance of TweetNER7 comes with a timestamp which distributes from September 2019 to August 2021.

• Entity Types: corperation, creative_work, event, group, location, product, person

Preprocessing

We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token {{URL}} and non-verified usernames into {{USERNAME}}. For verified usernames, we replace its display name (or account name) with symbols {@}. For example, a tweet

Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below: 
http://bluenote.lnk.to/AlbumOfTheWeek

is transformed into the following text.

Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}

A simple function to format tweet follows below.

import re
from urlextract import URLExtract
extractor = URLExtract()

def format_tweet(tweet):
    # mask web urls
    urls = extractor.find_urls(tweet)
    for url in urls:
        tweet = tweet.replace(url, "{{URL}}")
    # format twitter account
    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
    return tweet

target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'

We ask annotators to ignore those special tokens but label the verified users' mentions.

Data Split

split	number of instances	description
train_2020	4616	training dataset from September 2019 to August 2020
train_2021	2495	training dataset from September 2020 to August 2021
train_all	7111	combined training dataset of train_2020 and train_2021
validation_2020	576	validation dataset from September 2019 to August 2020
validation_2021	310	validation dataset from September 2020 to August 2021
validation_all	886	combined validation dataset of validation_2020 and validation_2021
test_2020	576	test dataset from September 2019 to August 2020
test_2021	2807	test dataset from September 2020 to August 2021
test_all	3383	combined test dataset of test_2020 and test_2021
train_random	4616	randomly sampled training dataset with the same size as train_2020 from train_all
validation_random	576	randomly sampled training dataset with the same size as validation_2020 from validation_all
extra_2020	87880	extra tweet without annotations from September 2019 to August 2020
extra_2021	93594	extra tweet without annotations from September 2020 to August 2021

Reproduce Experimental Result

To reproduce the experimental result on our AACL paper, please see the repository https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper.

Dataset Structure

Data Instances

An example of train looks as follows.

{
    'tokens': ['Morning', '5km', 'run', 'with', '{{USERNAME}}', 'for', 'breast', 'cancer', 'awareness', '#', 'pinkoctober', '#', 'breastcancerawareness', '#', 'zalorafit', '#', 'zalorafitxbnwrc', '@', 'The', 'Central', 'Park', ',', 'Desa', 'Parkcity', '{{URL}}'],
    'tags': [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 2, 14, 2, 14, 14, 14, 14, 14, 14, 4, 11, 11, 11, 11, 14],
    'id': '1183344337016381440',
    'date': '2019-10-13'
}

Label ID

The label2id dictionary can be found at here.

{
    "B-corporation": 0,
    "B-creative_work": 1,
    "B-event": 2,
    "B-group": 3,
    "B-location": 4,
    "B-person": 5,
    "B-product": 6,
    "I-corporation": 7,
    "I-creative_work": 8,
    "I-event": 9,
    "I-group": 10,
    "I-location": 11,
    "I-person": 12,
    "I-product": 13,
    "O": 14
}

Citation Information

@inproceedings{ushio-etal-2022-tweet,
    title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
    author = "Ushio, Asahi  and
        Neves, Leonardo  and
        Silva, Vitor  and
        Barbieri, Francesco. and
        Camacho-Collados, Jose",
    booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
    month = nov,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}