Semantic Annotation of UMLS using Conditional Random Fields
Shahad Kudama, Rafael Berlanga · 2014
In this work, we present a first approximation to the semantic annotation of Unified Medical Language System (UMLS®) concept descriptions based on the extraction of relevant linguistic features and its use in conditional random fields (CRF) to classify them at the different semantic groups provided by UMLS. Experiments have been carried out over the whole set of concepts of UMLS (more than 1 million). The precision scores obtained in the global system evaluation are high, between 70% and 80% approximately, depending on the percentage of semantic information provided as input. Regarding results by semantic group, the precision even reaches the 100% in those groups with highest representation in the selected descriptions of UMLS.