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 the official repository 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. The tweet collection used in TweetNER7 is same as what used in TweetTopic. The dataset is integrated in TweetNLP too.

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
test_2020	576	test dataset from September 2019 to August 2020
test_2021	2807	test dataset from September 2020 to August 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

For the temporal-shift setting, model should be trained on train_2020 with validation_2020 and evaluate on test_2021. In general, model would be trained on train_all, the most representative training set with validation_2021 and evaluate on test_2021.

Models

Following models are fine-tuned on train_all and validated on validation_2021 of tner/tweetner7. See full model list here.

Model (link)	Data	Language Model
`tner/roberta-large-tweetner7-all`	`tweetner7`	`roberta-large`
`tner/roberta-base-tweetner7-all`	`tweetner7`	`roberta-base`
`tner/twitter-roberta-base-2019-90m-tweetner7-all`	`tweetner7`	`twitter-roberta-base-2019-90m`
`tner/twitter-roberta-base-dec2020-tweetner7-all`	`tweetner7`	`twitter-roberta-base-dec2020`
`tner/twitter-roberta-base-dec2021-tweetner7-all`	`tweetner7`	`twitter-roberta-base-dec2021`

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",
}