The development of a multilingual terminology is a very long and costly process. We present the creation of a multilingual terminological database called GRISP covering multiple technical and scientific fields from various open resources. A crucial aspect is the merging of the different resources which is based in our proposal on the definition of a sound conceptual model, different domain mapping and the use of structural constraints and machine learning techniques for controlling the fusion process. The result is a massive terminological database of several millions terms, concepts, semantic relations and definitions. The accuracy of the concept merging between several resources have been evaluated following several methods. This resource has allowed us to improve significantly the mean average precision of an information retrieval system applied to a large collection of multilingual and multidomain patent documents. New specialized terminologies, not specifically created for text processing applications, can be aggregated and merged to GRISP with minimal manual efforts.

Existing parsing algorithms for Lexicalized Tree Grammars (LTG) formalisms (LTAG, TIG, DTG, ... ) are adaptations of algorithms initially dedicated to Context Free Grammars (CFG). They do not really take into account the fact that we do not use context free rules but partial parsing trees that we try to combine. Moreover the lexicalization raises up the important problem of multiplication of structures, a problem which does not exist in CFG. This paper presents parsing techniques for LTG taking into account these two fundamental features. Our approach focuses on robust and pratical purposes. Our parsing algorithm results in more extended partial parsing when the global parsing fails and in an interesting average complexity compared with others bottom-up algorithms.