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A major difficulty in applying word vector embeddings in IR is in devising aneffective and efficient strategy for obtaining representations of compoundunits of text, such as whole documents, (in comparison to the atomic words),for the purpose of indexing and scoring documents. Instead of striving for asuitable method for obtaining a single vector representation of a largedocument of text, we rather aim for developing a similarity metric that makesuse of the similarities between the individual embedded word vectors in adocument and a query. More specifically, we represent a document and a query assets of word vectors, and use a standard notion of similarity measure betweenthese sets, computed as a function of the similarities between each constituentword pair from these sets. We then make use of this similarity measure incombination with standard IR based similarities for document ranking. Theresults of our initial experimental investigations shows that our proposedmethod improves MAP by up to $5.77\%$, in comparison to standard text-basedlanguage model similarity, on the TREC ad-hoc dataset.

Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval