Interactive medical word sense disambiguation through informed learning | Zendy

Yue Wang | Zendy; Kai Zheng | Zendy; Hua Xu | Zendy; Qiaozhu Mei | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Interactive medical word sense disambiguation through informed learning

Author(s) -

Yue Wang,

Kai Zheng,

Hua Xu,

Qiaozhu Mei

Publication year - 2018

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocy013

Subject(s) - computer science , artificial intelligence , natural language processing , domain (mathematical analysis) , metric (unit) , set (abstract data type) , quality (philosophy) , machine learning , word sense disambiguation , search engine indexing , word (group theory) , information retrieval , mathematics , mathematical analysis , philosophy , operations management , geometry , epistemology , wordnet , economics , programming language

Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert's domain knowledge in building high-quality medical WSD models with minimal human effort.

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