A semi-automatic system for tagging specialized corpora

In this paper, we treat the problem of the grammatical tagging of non-annotated corpora of specialty. The existing taggers are trained on general language corpora, and give inconsistent results on the specialized texts, as technical and scientific ones. In order to learn rules adapted to a specialized field, the usual approach labels manually a large corpus of this field. This is extremely time-consuming. We propose here a semi-automatic approach for tagging corpora of specialty. ETIQ, the new tagger we are building, make it possible to correct the base of rules obtained by Brill's tagger and to adapt it to a corpus of specialty. The user visualizes an initial and basic tagging and corrects it either by extending Brill's lexicon or by the insertion of specialized lexical and contextual rules. The inserted rules are richer and more flexible than Brill's ones. To help the expert in this task, we designed an inductive algorithm biased by the "correct" knowledge he acquired beforehand. By using techniques of machine learning and enabling the expert to incorporate knowledge of the field in an interactive and friendly way, we improve the tagging of specialized corpora. Our approach has been applied to a corpus of molecular biology.