In this work, we reflect on ways to improve medical information retrieval accuracy 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 approaches to query reformulation and ranking 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 one of our novel algorithms, we are able to alleviate the negative impact of negations on early precision.
This paper has been accepted for presentation at the ACM SIGIR Medical Information Retrieval Workshop (MedIR) in Pisa, Italy.