Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning.

ABSTRACT: Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health information technology. Building an ontology facilitates creating interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for data labeling requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised KP identification framework using minimal labeled data based on hierarchical attention over the documents and domain adaptation. Our method outperforms the prior neural architectures by learning through synthetic labels for initial training, document-level contextual learning, language modeling, and fine-tuning with limited g

SUBMITTER: Goli R 

PROVIDER: S-EPMC10246160 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature