Quantitative assessment of dictionary-based protein named entity tagging.

ABSTRACT:

Objective

Natural language processing (NLP) approaches have been explored to manage and mine information recorded in biological literature. A critical step for biological literature mining is biological named entity tagging (BNET) that identifies names mentioned in text and normalizes them with entries in biological databases. The aim of this study was to provide quantitative assessment of the complexity of BNET on protein entities through BioThesaurus, a thesaurus of gene/protein names for UniProt knowledgebase (UniProtKB) entries that was acquired using online resources.

Methods

We evaluated the complexity through several perspectives: ambiguity (i.e., the number of genes/proteins represented by one name), synonymy (i.e., the number of names associated with the same gene/p

SUBMITTER: Liu H 

PROVIDER: S-EPMC1561801 | biostudies-literature | 2006 Sep-Oct

REPOSITORIES: biostudies-literature