  Ever-expanding volumes of biomedical text require automated semantic
annotation techniques to curate and put to best use. An established field of
research seeks to link mentions in text to knowledge bases such as those
included in the UMLS (Unified Medical Language System), in order to enable a
more sophisticated understanding. This work has yielded good results for tasks
such as curating literature, but increasingly, annotation systems are more
broadly applied. Medical vocabularies are expanding in size, and with them the
extent of term ambiguity. Document collections are increasing in size and
complexity, creating a greater need for speed and robustness. Furthermore, as
the technologies are turned to new tasks, requirements change; for example
greater coverage of expressions may be required in order to annotate patient
records, and greater accuracy may be needed for applications that affect
patients. This places new demands on the approaches currently in use. In this
work, we present a new system, Bio-YODIE, and compare it to two other popular
systems in order to give guidance about suitable approaches in different
scenarios and how systems might be designed to accommodate future needs.
