DNorm: disease name normalization with pairwise learning to rank | Zendy

Robert Leaman | Zendy; Rezarta Islamaj | Zendy; Zhiyong Lu | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

DNorm: disease name normalization with pairwise learning to rank

Author(s) -

Robert Leaman,

Rezarta Islamaj,

Zhiyong Lu

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt474

Subject(s) - normalization (sociology) , pairwise comparison , computer science , natural language processing , vocabulary , information retrieval , named entity recognition , source code , artificial intelligence , controlled vocabulary , task (project management) , programming language , linguistics , philosophy , management , sociology , anthropology , economics

Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research