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Free keywords:
Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
Abstract:
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.
