  In this paper, we reflect on ways to improve the quality of bio-medical
information retrieval by drawing implicit negative feedback from negated
information in noisy natural language search queries. We begin by studying the
extent to which negations occur in clinical texts and quantify their
detrimental effect on retrieval performance. Subsequently, we present a number
of query reformulation and ranking approaches that remedy these shortcomings by
resolving natural language negations. Our experimental results are based on
data collected in the course of the TREC Clinical Decision Support Track and
show consistent improvements compared to state-of-the-art methods. Using our
novel algorithms, we are able to reduce the negative impact of negations on
early precision by up to 65%.
