  In recent years, quantum-based methods have promisingly integrated the
traditional procedures in information retrieval (IR) and natural language
processing (NLP). Inspired by our research on the identification and
application of quantum structures in cognition, more specifically our work on
the representation of concepts and their combinations, we put forward a
'quantum meaning based' framework for structured query retrieval in text
corpora and standardized testing corpora. This scheme for IR rests on
considering as basic notions, (i) 'entities of meaning', e.g., concepts and
their combinations and (ii) traces of such entities of meaning, which is how
documents are considered in this approach. The meaning content of these
'entities of meaning' is reconstructed by solving an 'inverse problem' in the
quantum formalism, consisting of reconstructing the full states of the entities
of meaning from their collapsed states identified as traces in relevant
documents. The advantages with respect to traditional approaches, such as
Latent Semantic Analysis (LSA), are discussed by means of concrete examples.
