Even though many NLP resources and tools claim to be domain independent, their application to specifc tasks is restricted to
some specifc domain, otherwise their performance degrade notably. As the accuracy of NLP resources drops heavily when
applied in environments diferent from which they were built a tuning to the new environment is needed. This paper proposes
a method for automatically compile terminologies from potentially any domain. The proposed method takes as reference the
set of domains ...
Even though many NLP resources and tools claim to be domain independent, their application to specifc tasks is restricted to
some specifc domain, otherwise their performance degrade notably. As the accuracy of NLP resources drops heavily when
applied in environments diferent from which they were built a tuning to the new environment is needed. This paper proposes
a method for automatically compile terminologies from potentially any domain. The proposed method takes as reference the
set of domains defned by Magnini, the Multilingual Central Repository (a resource based on WordNet 3.0) together with
DBpedia, an open knowledge source that had proven to be reliable for restricted domains. Using the method described in
this article, we have produced a big set of reliable terminologies for 164 domains and 2 languages totalling 635,527 terms.
The proposed method has been applied to English and Spanish languages but it is potentially applicable to any language that
has its own a DBpedia evolved enough. The obtained results have been intensively evaluated in several ways.
+
