Update README.md

README.md

@@ -25,7 +25,78 @@ pretty_name: TweetTopicSingle
 
 
 ### Dataset Summary
-Topic classification dataset on Twitter with multiple labels per tweet. See [cardiffnlp/tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single) for single label version of Tweet Topic.
+This is the official repository of TweetTopic ("Twitter Topic Classification
+, COLING main conference 2022"), a topic classification dataset on Twitter with 19 labels.
+Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
+See [cardiffnlp/tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single) for single label version of TweetTopic.
+
+### 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.
+
+```python
+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}}'
+```
+
+### Data Splits
+
+| split                   | number of texts | description |
+|:------------------------|-----:|------:|
+| test_2020               |  573 | test dataset from September 2019 to August 2020 |
+| test_2021               | 1679 | test dataset from September 2020 to August 2021 |
+| train_2020              | 4585 | training dataset from September 2019 to August 2020 |
+| train_2021              | 1505 | training dataset from September 2020 to August 2021 |
+| train_all               | 6090 | combined training dataset of `train_2020` and `train_2021` |
+| validation_2020         |  573 | validation dataset from September 2019 to August 2020 |
+| validation_2021         |  188 | validation dataset from September 2020 to August 2021 | 
+| train_random            | 4564 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
+| validation_random       |  573 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
+| test_coling2022_random  | 5536 | random split used in the COLING 2022 paper |
+| train_coling2022_random | 5731 | random split used in the COLING 2022 paper |
+| test_coling2022         | 5536 | temporal split used in the COLING 2022 paper |
+| train_coling2022        | 5731 | temporal split used in the COLING 2022 paper |
+
+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`.
+
+**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
+
+### Models
+
+
 
 ## Dataset Structure
 
@@ -68,48 +139,6 @@ The label2id dictionary can be found at [here](https://huggingface.co/datasets/t
  }
 ```
 
-### Data Splits
-
-
-| split                       | number of texts | description |
-|:----------------------------|-----:|:-----|
-| `test`                      | 1679 | alias of `temporal_2021_test` |
-| `train`                     | 4585 | alias of `temporal_2020_train` | 
-| `validation`                |  573 | alias of `temporal_2020_validation` |
-| `temporal_2020_test`        |  573 | test set in 2020 period of temporal split |
-| `temporal_2021_test`        | 1679 | test set in 2021 period of temporal split |
-| `temporal_2020_train`       | 4585 | training set in 2020 period of temporal split |
-| `temporal_2021_train`       | 1505 | training set in 2021 period of temporal split |
-| `temporal_2020_validation`  |  573 | validation set in 2020 period of temporal split |
-| `temporal_2021_validation`  |  188 | validation set in 2021 period of temporal split |
-| `random_train`              | 4564 | training set of random split (mix of 2020 and 2021) |
-| `random_validation`         |  573 | validation set of random split (mix of 2020 and 2021) |
-| `coling2022_random_test`    | 5536 | test set of random split used in COLING 2022 Tweet Topic paper |
-| `coling2022_random_train`   | 5731 | training set of random split used in COLING 2022 Tweet Topic paper |
-| `coling2022_temporal_test`  | 5536 | test set of temporal split used in COLING 2022 Tweet Topic paper |
-| `coling2022_temporal_train` | 5731 | training set of temporal split used in COLING 2022 Tweet Topic paper|
-
-| split                   | number of texts | description |
-|:------------------------|-----:|------:|
-| test_2020               |  573 | test dataset from September 2019 to August 2020 |
-| test_2021               | 1679 | test dataset from September 2020 to August 2021 |
-| train_2020              | 4585 | training dataset from September 2019 to August 2020 |
-| train_2021              | 1505 | training dataset from September 2020 to August 2021 |
-| train_all               | 6090 | combined training dataset of `train_2020` and `train_2021` |
-| validation_2020         |  573 | validation dataset from September 2019 to August 2020 |
-| validation_2021         |  188 | validation dataset from September 2020 to August 2021 | 
-| train_random            | 4564 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
-| validation_random       |  573 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
-| test_coling2022_random  | 5536 | random split used in the COLING paper |
-| train_coling2022_random | 5731 | random split used in the COLING paper |
-| test_coling2022         | 5536 | temporal split used in the COLING paper |
-| train_coling2022        | 5731 | temporal split used in the COLING paper |
-
-For the temporal-shift setting, we recommend to train models on `train` (an alias of `temporal_2020_train`) with `validation` (an alias of `temporal_2020_validation`) and evaluate on `test` (an alias of `temporal_2021_test`).
-For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).
-
-**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `coling2022_temporal_train` and `coling2022_temporal_test` for temporal-shift, and `coling2022_random_train` and `coling2022_temporal_test` fir random split (the coling2022 split does not have validation set).
-
 
 ### Citation Information