Biomedical Event Extraction as Sequence Labeling

Implemented By:

• AR Alan Ramponi
  ramponi@cosbi.eu
  Centre for Computational and Systems Biology (COSBI) and University of Trento
• RG Rob van der Goot
  robv@itu.dk
  IT University of Copenhagen
• RL Rosario Lombardo
  lombardo@cosbi.eu
  Centre for Computational and Systems Biology (COSBI) and University of Trento
• BP Barbara Plank
  bapl@itu.dk
  IT University of Copenhagen

Description:

We introduce Biomedical Event Extraction as Sequence Labeling (BEESL), a joint end to-end neural information extraction model. BEESL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning. BEESL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools. BEESL outperforms the current best system (Li et al., 2019) on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1, establishing a new state of the art for the task. Importantly, we also provide first results on biomedical event extraction without gold entity information. Empirical results show that BEESL’s speed and accuracy makes it a viable approach for large-scale real-world scenarios.