  The availability of an abundance of knowledge sources has spurred a large
amount of effort in the development and enhancement of Information Retrieval
techniques. Users information needs are expressed in natural language and
successful retrieval is very much dependent on the effective communication of
the intended purpose. Natural language queries consist of multiple linguistic
features which serve to represent the intended search goal. Linguistic
characteristics that cause semantic ambiguity and misinterpretation of queries
as well as additional factors such as the lack of familiarity with the search
environment affect the users ability to accurately represent their information
needs, coined by the concept intention gap. The latter directly affects the
relevance of the returned search results which may not be to the users
satisfaction and therefore is a major issue impacting the effectiveness of
information retrieval systems. Central to our discussion is the identification
of the significant constituents that characterize the query intent and their
enrichment through the addition of meaningful terms, phrases or even latent
representations, either manually or automatically to capture their intended
meaning. Specifically, we discuss techniques to achieve the enrichment and in
particular those utilizing the information gathered from statistical processing
of term dependencies within a document corpus or from external knowledge
sources such as ontologies. We lay down the anatomy of a generic linguistic
based query expansion framework and propose its module-based decomposition,
covering topical issues from query processing, information retrieval,
computational linguistics and ontology engineering. For each of the modules we
review state-of-the-art solutions in the literature categorized and analyzed
under the light of the techniques used.
