Dataset Card for "tner/tweetner7"

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

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 .

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
}

Models

See full evaluation metrics here .

Main Models

Model description follows below.

• Model with suffix -all : Model fine-tuned on train_all and validated on validation_2021 .
• Model with suffix -continuous : Model fine-tuned on train_2021 continuously after fine-tuning on train_2020 and validated on validation_2021 .
• Model with suffix -2021 : Model fine-tuned only on train_2021 and validated on validation_2021 .
• Model with suffix -2020 : Model fine-tuned only on train_2021 and validated on validation_2020 .

Sub Models (used in ablation study)

• Model fine-tuned only on train_random and validated on validation_2020 .

• Model fine-tuned on the self-labeled dataset on extra_{2020,2021} and validated on validation_2020 .

Model description follows below.

• Model with suffix -self2020 : Fine-tuning on the self-annotated data of extra_2020 split of tweetner7 .
• Model with suffix -self2021 : Fine-tuning on the self-annotated data of extra_2021 split of tweetner7 .
• Model with suffix -2020-self2020-all : Fine-tuning on the self-annotated data of extra_2020 split of tweetner7 . Combined training dataset of extra_2020 and train_2020 .
• Model with suffix -2020-self2021-all : Fine-tuning on the self-annotated data of extra_2021 split of tweetner7 . Combined training dataset of extra_2021 and train_2020 .
• Model with suffix -2020-self2020-continuous : Fine-tuning on the self-annotated data of extra_2020 split of tweetner7 . Fine-tuning on train_2020 and continuing fine-tuning on extra_2020 .
• Model with suffix -2020-self2021-continuous : Fine-tuning on the self-annotated data of extra_2021 split of tweetner7 . Fine-tuning on train_2020 and continuing fine-tuning on extra_2020 .

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 .

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",
}