  In this paper we summarized a framework for designing grammar-based procedure
for the automatic extraction of the semantic content from spoken queries.
Starting with a case study and following an approach which combines the notions
of fuzziness and robustness in sentence parsing, we showed we built practical
domain-dependent rules which can be applied whenever it is possible to
superimpose a sentence-level semantic structure to a text without relying on a
previous deep syntactical analysis. This kind of procedure can be also
profitably used as a pre-processing tool in order to cut out part of the
sentence which have been recognized to have no relevance in the understanding
process. In the case of particular dialogue applications where there is no need
to build a complex semantic structure (e.g. word spotting or excerpting) the
presented methodology may represent an efficient alternative solution to a
sequential composition of deep linguistic analysis modules. Even if the query
generation problem may not seem a critical application it should be held in
mind that the sentence processing must be done on-line. Having this kind of
constraints we cannot design our system without caring for efficiency and thus
provide an immediate response. Another critical issue is related to whole
robustness of the system. In our case study we tried to make experiences on how
it is possible to deal with an unreliable and noisy input without asking the
user for any repetition or clarification. This may correspond to a similar
problem one may have when processing text coming from informal writing such as
e-mails, news and in many cases Web pages where it is often the case to have
irrelevant surrounding information.
