  Knowledge workers (such as healthcare information professionals, patent
agents and recruitment professionals) undertake work tasks where search forms a
core part of their duties. In these instances, the search task is often complex
and time-consuming and requires specialist expert knowledge to formulate
accurate search strategies. Interactive features such as query expansion can
play a key role in supporting these tasks. However, generating query
suggestions within a professional search context requires that consideration be
given to the specialist, structured nature of the search strategies they
employ. In this paper, we investigate a variety of query expansion methods
applied to a collection of Boolean search strategies used in a variety of
real-world professional search tasks. The results demonstrate the utility of
context-free distributional language models and the value of using linguistic
cues such as ngram order to optimise the balance between precision and recall.
