Name
Abstract | ||
|---|---|---|
This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task. We used several types of pre-trained language models, which are trained on Spanish biomedical literature or Spanish Tweets. We showed the difference in performance depending on the quality of the tokenization as well as introducing silver standard annotations when training the model. Our model obtained a strict F1 of 80.3% on the test set, which is an improvement of +12.8% F1 (24.6 std) over the average results across all submissions to the SocialDisNER challenge. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antoine Lain | 1 | 0 | 0.34 |
| Wonjin Yoon | 2 | 0 | 0.34 |
| Hyunjae Kim | 3 | 0 | 0.68 |
| Jaewoo Kang | 4 | 1258 | 179.45 |
| Ian Simpson | 5 | 0 | 0.34 |