Concept recognition as a machine translation problem.

ABSTRACT:

Background

Automated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models have the potential to outperform multi-class classification approaches.

Methods

We systematically characterize the factors t

SUBMITTER: Boguslav MR 

PROVIDER: S-EPMC8678974 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature