| Challenge: | Empirical results show that BeeSL’s speed and accuracy makes it a viable approach for large-scale real-world scenarios. |
| Approach: | They propose a joint end-to-end neural information extraction model that recasts the task as sequence labeling and jointly models intermediate tasks via multi-task learning. |
| Outcome: | Empirical results show that BeeSL outperforms the current best system on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1 . |
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