RE-PACRR: A Context and Density-Aware Neural Information Retrieval Model
Ad-hoc retrieval models can benefit from considering different patterns in the interactions between a query and a document, effectively assessing the relevance of a document for a given user query. Factors to be considered in this interaction include (i) the matching of unigrams and ngrams, (ii) the proximity of the matched query terms, (iii) their position in the document, and (iv) how the different relevance signals are combined over different query terms. While previous work has successfully modeled some of these factors, not all aspects have been fully explored. In this work, we close this gap by proposing different neural components and incorporating them into a single architecture, leading to a novel neural IR model called RE-PACRR. Extensive comparisons with established models on TREC Web Track data confirm that the proposed model yields promising search results.