Paper Info
Title
Determining the syntactic structure of medical terms in clinical notes
Abstract
This paper demonstrates a method for determining the syntactic structure of medical terms. We use a model-fitting method based on the Log Likelihood Ratio to classify three-word medical terms as right or left-branching. We validate this method by computing the agreement between the classification produced by the method and manually annotated classifications. The results show an agreement of 75%--83%. This method may be used effectively to enable a wide range of applications that depend on the semantic interpretation of medical terms including automatic mapping of terms to standardized vocabularies and induction of terminologies from unstructured medical text.
Year
Venue
Keywords
2007
BioNLP@ACL
automatic mapping,standardized vocabulary,log likelihood ratio,model-fitting method,syntactic structure,clinical note,annotated classification,unstructured medical text,three-word medical term,semantic interpretation,medical term
Field
DocType
Citations 
Information retrieval,Likelihood-ratio test,Computer science,Semantic interpretation,Artificial intelligence,Natural language processing,Syntactic structure
Conference
3
PageRank 
References 
Authors
0.43
16
3
Name
Order
Citations
PageRank
Bridget T. McInnes128023.66
Ted Pedersen22738220.47
Sergey V. Pakhomov3555.99