Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities

Xiao Liu (1, 2) , Antoine Bordes (2, 1) , Yves Grandvalet (2, 1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"

This paper describes the HDS4NLP en- try to the BioNLP 2013 shared task on biomedical event extraction. This system is based on a pairwise model that transforms trigger classification in a simple multi-class problem in place of the usual multi-label problem. This model facilitates inference compared to global models while relying on richer information compared to usual pipeline approaches. The HDS4NLP system ranked 6th on the Genia task (43.03% f-score), and after fixing a bug discovered after the final submission, it outperforms the winner of this task (with a f-score of 51.15%).

Domaines: Apprentissage [cs.LG]

Xiao Liu, Antoine Bordes, Yves Grandvalet. Biomedical Event Extraction by Multi-class Classification of Pairs of Text Entities. BioNLP Shared Task 2013 Workshop, Aug 2013, Sofia, Bulgaria. pp.45-49. ⟨hal-00880444⟩