This paper presents a series of tools for the extraction of specialized corpora from the web and its subsequent analysis mainly with statistical techniques. It is an integrated system of original as well as standard tools and has a modular conception that facilitates its re-integration on different systems. The first part of the paper describes the original techniques, which are devoted to the categorization of documents as relevant or irrelevant to the corpus under construction, considering relevant a specialized document of the selected technical domain. Evaluation figures are provided for the original part, but not for the second part involving the analysis of the corpus, which is composed of algorithms that are well known in the field of Natural Language Processing, such as Kwic search, measures of vocabulary richness, the sorting of n-grams by frequency of occurrence or by measures of statistical association, distribution or similarity.

The main goal of this paper is to present a first approach to an automatic detection of conceptual relations between two terms in specialised written text. Previous experiments on the basis of the manual analysis lead the authors to implement an automatic query strategy combining the term candidates proposed by an extractor together with a list of verbal syntactic patterns used for the relations refinement. Next step on the research will be the integration of the results into the term extractor in order to attain more restrictive pieces of information directly reused for the ontology building task.