Paper Info
Title
Medical Entities Tagging Using Distant Learning
Abstract
A semantic tagger aiming to detect relevant entities in medical documents and tagging them with their appropriate semantic class is presented. In the experiments described in this paper the tagset consists of the six most frequent classes in SNOMED-CT taxonomy (SN). The system uses six binary classifiers, and two combination mechanisms are presented for combining the results of the binary classifiers. Learning the classifiers is performed using three widely used knowledge sources, including one domain restricted and two domain independent resources. The system obtains state-of-the-art results.
Year
DOI
Venue
2015
10.1007/978-3-319-18117-2_47
COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING (CICLING 2015), PT II
Field
DocType
Volume
Medical documents,Information retrieval,Computer science,Artificial intelligence,Natural language processing,Distant learning,Binary number
Conference
9042
ISSN
Citations 
PageRank 
0302-9743
2
0.40
References 
Authors
12
2
Name
Order
Citations
PageRank
Jorge Vivaldi17715.17
Horacio Rodríguez2131.76