Air pollution has a major influence on health. It is thus not surprising that air quality (AQ) increasingly becomes a central issue in the environmental information policy worldwide. The most common way to deliver AQ information is in terms of graphics, tables, pictograms, or color scales that display either the concentrations of the pollutant substances or the corresponding AQ indices. However, all of these presentation modi lack the explanatory dimension; nor can they be easily tailored to the needs of the individual users. MARQUIS is an AQ information generation service that produces user-tailored multilingual bulletins on the major measured and forecasted air pollution substances and their relevance to human health in five European regions. It incorporates modules for the assessment of pollutant time series episodes with respect to their relevance to a given addressee, for planning of the discourse structure of the bulletins and the selection of the adequate presentation mode, and for generation proper. The positive evaluation of the bulletins produced by MARQUIS by users shows that the use of automatic text generation techniques in such a complex and sensitive application is feasible.

This article explores a statistical, language-independent methodology for the construction of taxonomies of specialized domains from noisy corpora. In contrast to proposals that exploit linguistic information by searching for lexico-syntactic patterns that tend to express the hypernymy relation, our methodology relies entirely upon the distributional semantics of terms as captured by their lexical co-occurrence in large scale corpora. In a first stage, we analyze the syntagmatic relations of terms that serve as seeds of the taxonomy to be constructed and we obtain, thus, the first batch of hypernym candidate terms for our seed terms. In a second stage, we analyze the paradigmatic relations of the terms by inspecting which terms show a prominent frequency of co-occurrence with the terms that, as we found in the previous stage, are syntagmatically related to our seed terms — which allows us to refine the first batch of hypernym candidate terms and obtain new ones. In a third and final stage, we build a taxonomy from the obtained hypernym candidate lists, exploiting the asymmetric statistic association between terms that is characteristic of the hypernymy relation.