Semantic annotation using biomedical ontologies is required to enable data integration, interoperability, indexing and mining of biomedical data. When used to support semantic indexing the scoring and ranking of annotations become as important as provenance and metadata on the annotations themselves. In the biomedical domain, one broadly used service for annotations is the NCBO Annotator Web service, offered within the BioPortal platform and giving access to more than 350+ ontologies or terminologies. This paper presents a new scoring method for the NCBO Annotator allowing to rank the annotation results and enabling to use such scores for better indexing of the annotated data. By using a natural language processing-based term extraction measure, C-Value, we are able to enhance the original scoring algorithm which uses basic frequencies of the matches and in addition to positively discriminate multi-words term annotations. We show results obtained by comparing three different methods with a reference corpus of PubMed-MeSH manual annotations.