  In this paper, we propose a linguistically-motivated query expansion
framework that recognizes and en-codes significant query constituents that
characterize query intent in order to improve retrieval performance.
Concepts-of-Interest are recognized as the core concepts that represent the
gist of the search goal whilst the remaining query constituents which serve to
specify the search goal and complete the query structure are classified as
descriptive, relational or structural. Acknowledging the need to form
semantically-associated base pairs for the purpose of extracting related
potential expansion concepts, an algorithm which capitalizes on syntactical
dependencies to capture relationships between adjacent and non-adjacent query
concepts is proposed. Lastly, a robust weighting scheme that duly emphasizes
the importance of query constituents based on their linguistic role within the
expanded query is presented. We demonstrate improvements in retrieval
effectiveness in terms of increased mean average precision (MAP) garnered by
the proposed linguistic-based query expansion framework through experimentation
on the TREC ad hoc test collections.
