Open AccessA Multi-aspect Comparison Study of Supervised Word Sense Disambiguation
Author(s) -
H. Liu
Publication year - 2004
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 - 10.1197/jamia.m1533
Subject(s) - computer science , artificial intelligence , natural language processing , set (abstract data type) , supervised learning , context (archaeology) , paragraph , term (time) , semeval , labeled data , wordnet , feature (linguistics) , word sense disambiguation , machine learning , word (group theory) , window (computing) , mathematics , artificial neural network , linguistics , paleontology , philosophy , physics , geometry , management , quantum mechanics , world wide web , economics , biology , programming language , task (project management) , operating system
The aim of this study was to investigate relations among different aspects in supervised word sense disambiguation (WSD; supervised machine learning for disambiguating the sense of a term in a context) and compare supervised WSD in the biomedical domain with that in the general English domain.