Implicit Negative Feedback in Clinical Information Retrieval
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Abstract
In this paper, 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 quantifying 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. For queries that make excessive use of negations, we were able to achieve up to 300% relative improvement in early precision.
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