Evaluation of string normalisation modules for string-based biomedical vocabularies alignment with AnAGram

Biomedical vocabularies have specific characteristics that make their lexical alignment challenging. We have built a string-based vocabulary alignment tool, AnAGram, dedicated to efficiently compare terms in the biomedical domain, and evaluate this tool's results against an algorithm based on Jaro-Winkler's edit-distance. AnAGram is modular, enabling us to evaluate the precision and recall of different normalization procedures. Globally, our normalization and replacement strategy improves the F-measure score from the edit-distance experiment by more than 100%. Most of this increase can be explained by targeted transformations of the strings with the use of a dictionary of adjective/noun correspondences yielding useful results. However, we found that the classic Porter stemming algorithm needs to be adapted to the biomedical domain to give good quality results in this area.