Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K - 1M
ArXiv:
License:
Update README.md
Browse files
README.md
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### Dataset Summary
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This is
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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
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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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### Models
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