The relative ineffectiveness of information retrieval systems is largely caused by the
inaccuracy with which a query formed by a few keywords models the actual user information …

We present a suite of query expansion methods that are based on word embeddings. Using
Word2Vec's CBOW embedding approach, applied over the entire corpus on which search is …

Pseudo-relevance feedback has proven to be an effective strategy for improving retrieval
accuracy in all retrieval models. However the performance of existing pseudo feedback …

Typical pseudo-relevance feedback methods assume the top-retrieved documents are
relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set …

Pseudo-relevance feedback (PRF) is a well-known method for addressing the mismatch
between query intention and query representation. Most current PRF methods consider …

The sheer volume of scholarly publications available online significantly challenges how
scholars retrieve the new information available and locate the candidate reference papers …

Existing pseudo-relevance feedback methods typically perform averaging over the top-
retrieved documents, but ignore an important statistical dimension: the risk or variance …

Thee KL divergence is the most commonly used measure for comparing query and
document language models in the language modeling framework to ad hoc retrieval. Since …

This paper proposes a new machine learning method for constructing ranking models in
document retrieval. The method, which is referred to as SSRank, aims to use the advantages …

Schema matching is at the heart of integrating structured and semi-structured data with
applications in data warehousing, data analysis recommendations, Web table matching, etc …