Paper: Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction

ACL ID E14-1073
Title Fast Recursive Multi-class Classification of Pairs of Text Entities for Biomedical Event Extraction
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Biomedical event extraction from arti- cles has become a popular research topic driven by important applications, such as the automatic update of dedicated knowl- edge base. Most existing approaches are either pipeline models of specific classi- fiers, usually subject to cascading errors, or joint structured models, more efficient but also more costly and more involved to train. We propose here a system based on a pairwise model that transforms event ex- traction into a simple multi-class problem of classifying pairs of text entities. Such pairs are recursively provided to the classi- fier, so as to extract events involving other events as arguments. Our model is more direct than the usual pipeline approaches, and speeds up inference compared to joint models. We report here the best result...