Using rule-based natural language processing to improve disease normalization in biomedical text.

ABSTRACT:

Background and objective

In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization.

Methods

We compared the performance of two biomedical concept normalization systems, MetaMap and Peregrine, on the Arizona Disease Corpus, with and without the use of a rule-based NLP module. Performance was assessed for exact and inexact boundary matching of the system annotations with those of the gold standard and for

SUBMITTER: Kang N 

PROVIDER: S-EPMC3756254 | biostudies-literature | 2013 Sep-Oct

REPOSITORIES: biostudies-literature