Improving retrievability of patents with cluster-based pseudo-relevance feedback documents selection

High findability of documents within a certain cut-off rank is considered an important factor in recall-oriented appli- cation domains such as patent or legal document retrieval. Findability is hindered by two aspects, namely the inher- ent bias favoring some types of documents over others in- troduced by the retrieval model, and the failure to cor- rectly capture and interpret the context of conventionally rather short queries. In this paper, we analyze the bias impact of different retrieval models and query expansion strategies. We furthermore propose a novel query expan- sion strategy based on document clustering to identify dom- inant relevant documents. This helps to overcome limita- tions of conventional query expansion strategies that suffer strongly from the noise introduced by imperfect initial query results for pseudo-relevance feedback documents selection. Experiments with different collections of patent documents suggest that clustering based document selection for pseudo- relevance feedback is an effective approach for increasing the findability of individual documents and decreasing the bias of a retrieval system.