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@@ -26,9 +26,8 @@ pretty_name: TweetNER7
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  ### Dataset Summary
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- This is an official of TweetNER7 ("Named Entity Recognition in Twitter:
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- 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
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- a timestamp which distributes from September 2019 to August 2021.
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  - Entity Types: `corperation`, `creative_work`, `event`, `group`, `location`, `product`, `person`
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  ### Preprocessing
@@ -92,6 +91,9 @@ We ask annotators to ignore those special tokens but label the verified users' m
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  | extra_2020 | 87880 | extra tweet without annotations from September 2019 to August 2020 |
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  | extra_2021 | 93594 | extra tweet without annotations from September 2020 to August 2021 |
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  ### Models
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  ### Dataset Summary
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+ This is the official repository of TweetNER7 ("Named Entity Recognition in Twitter:
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+ 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.
 
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  - Entity Types: `corperation`, `creative_work`, `event`, `group`, `location`, `product`, `person`
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  ### Preprocessing
 
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  | extra_2020 | 87880 | extra tweet without annotations from September 2019 to August 2020 |
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  | extra_2021 | 93594 | extra tweet without annotations from September 2020 to August 2021 |
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+ For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
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+ In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
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+
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  ### Models
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