Semi-automatic semantic annotation of PubMed queries: a study on quality, efficiency, satisfaction.

ABSTRACT: Information processing algorithms require significant amounts of annotated data for training and testing. The availability of such data is often hindered by the complexity and high cost of production. In this paper, we investigate the benefits of a state-of-the-art tool to help with the semantic annotation of a large set of biomedical queries. Seven annotators were recruited to annotate a set of 10,000 PubMed® queries with 16 biomedical and bibliographic categories. About half of the queries were annotated from scratch, while the other half were automatically pre-annotated and manually corrected. The impact of the automatic pre-annotations was assessed on several aspects of the task: time, number of actions, annotator satisfaction, inter-annotator agreement, quality and number of the resul

SUBMITTER: Neveol A 

PROVIDER: S-EPMC3063330 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature