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README.md
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- **Paper:** TBA
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- **Dataset:** Tweet Topic Dataset
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- **Domain:** Twitter
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- **Number of Class:**
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### Dataset Summary
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### Data Splits
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| split | number of texts |
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| test | 1679 |
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| train | 1505 |
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| validation | 188 |
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| temporal_2020_test | 573 |
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| temporal_2021_test | 1679 |
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| temporal_2020_train | 4585 |
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| temporal_2021_train | 1505 |
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| temporal_2020_validation | 573 |
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| temporal_2021_validation | 188 |
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| random_train | 4564 |
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| random_validation | 573 |
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| coling2022_random_test | 5536 |
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| coling2022_random_train | 5731 |
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| coling2022_temporal_test | 5536 |
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| coling2022_temporal_train | 5731 |
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### Citation Information
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- **Paper:** TBA
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- **Dataset:** Tweet Topic Dataset
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- **Domain:** Twitter
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- **Number of Class:** 19
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### Dataset Summary
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### Data Splits
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| split | number of texts | description |
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| test | 1679 | alias of `temporal_2021_test` |
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| train | 1505 | alias of `temporal_2020_train` |
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| validation | 188 | alias of `temporal_2020_validation` |
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| temporal_2020_test | 573 | test set in 2020 period of temporal split |
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| temporal_2021_test | 1679 | test set in 2021 period of temporal split |
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| temporal_2020_train | 4585 | training set in 2020 period of temporal split |
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| temporal_2021_train | 1505 | training set in 2021 period of temporal split |
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| temporal_2020_validation | 573 | validation set in 2020 period of temporal split |
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| temporal_2021_validation | 188 | validation set in 2021 period of temporal split |
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| random_train | 4564 | training set of random split (mix of 2020 and 2021) |
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| random_validation | 573 | validation set of random split (mix of 2020 and 2021) |
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| coling2022_random_test | 5536 | test set of random split used in COLING 2022 Tweet Topic paper |
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| coling2022_random_train | 5731 | training set of random split used in COLING 2022 Tweet Topic paper |
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| coling2022_temporal_test | 5536 | test set of temporal split used in COLING 2022 Tweet Topic paper |
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| coling2022_temporal_train | 5731 | training set of temporal split used in COLING 2022 Tweet Topic paper|
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For the temporal-shift setting, we recommend to train models on `train` (`temporal_2020_train`) with `validation` (`temporal_2020_validation`) and evaluate on `test` (`temporal_2021_test`).
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For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).
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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 (note that the coling2022 split does not have validation set).
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### Citation Information
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