  Good term selection is an important issue for an automatic query expansion
(AQE) technique. AQE techniques that select expansion terms from the target
corpus usually do so in one of two ways. Distribution based term selection
compares the distribution of a term in the (pseudo) relevant documents with
that in the whole corpus / random distribution. Two well-known
distribution-based methods are based on Kullback-Leibler Divergence (KLD) and
Bose-Einstein statistics (Bo1). Association based term selection, on the other
hand, uses information about how a candidate term co-occurs with the original
query terms. Local Context Analysis (LCA) and Relevance-based Language Model
(RM3) are examples of association-based methods. Our goal in this study is to
investigate how these two classes of methods may be combined to improve
retrieval effectiveness. We propose the following combination-based approach.
Candidate expansion terms are first obtained using a distribution based method.
This set is then refined based on the strength of the association of terms with
the original query terms. We test our methods on 11 TREC collections. The
proposed combinations generally yield better results than each individual
method, as well as other state-of-the-art AQE approaches. En route to our
primary goal, we also propose some modifications to LCA and Bo1 which lead to
improved performance.
