Feature Weighting in Finding Feedback Documents for Query Expansion in Biomedical Document Retrieval

Abstract

Finding good feedback documents for query expansion is a well-known problem in the field of information retrieval. This paper describes a novel approach for finding relevant documents for feedback in query expansion for biomedical document retrieval. The proposed approach relies on a small amount of human intervention to find good feedback documents and tries to learn the relation between query and documents in terms of usefullness of document for query expansion. This proposed approach uses an NLP-based feature weighting technique with classification and clustering method on the documents and identifies relevant documents for feedback. The documents are represented using term frequency and inverse document frequency (TF–IDF) features and these features are weighted according to the type of query and type of the terms. The experiments performed on CDS 2014, 2015 and 2016 datasets show that the feature weighting in finding feedback documents for query expansion approach gives good results as compared to the results of pseudo-relevance feedback, relevance feedback and the results of TF–IDF features without weighting.