Improving chemical entity recognition through h-index based semantic similarity.

ABSTRACT:

Background

Our approach to the BioCreative IV challenge of recognition and classification of drug names (CHEMDNER task) aimed at achieving high levels of precision by applying semantic similarity validation techniques to Chemical Entities of Biological Interest (ChEBI) mappings. Our assumption is that the chemical entities mentioned in the same fragment of text should share some semantic relation. This validation method was further improved by adapting the semantic similarity measure to take into account the h-index of each ancestor. We applied this method in two measures, simUI and simGIC, and validated the results obtained for the competition, comparing each adapted measure to its original version.

Results

For the competition, we trained a Random Forest classifier that use

SUBMITTER: Lamurias A 

PROVIDER: S-EPMC4331689 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature