Studies of different term extractors on a corpus of the biomedical domain
revealed decreasing performances when applied to highly technical texts. The
difficulty or impossibility of customising them to new domains is an additional
limitation. In this paper, we propose to use external terminologies to
influence generic linguistic data in order to augment the quality of the
extraction. The tool we implemented exploits testified terms at different steps
of the process: chunking, parsing and extraction of term candidates.
Experiments reported here show that, using this method, more term candidates
can be acquired with a higher level of reliability. We further describe the
extraction process involving endogenous disambiguation implemented in the term
extractor YaTeA