Papers › Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets
Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets
Pius von D{\"a}niken, Mark Cieliebak
We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Named Entity Recognition (NER) | WNUT 2017 | SpinningBytes | F1 | 40.78 | #23 of 23 | Archive leaderboard | report |
| Named Entity Recognition (NER) | WNUT 2017 | SpinningBytes | F1 (surface form) | 39.33 | #23 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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