Automatically producing semantically tagged bilingual terminologies
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 bui...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/53145 |
| Acceso en línea: | http://hdl.handle.net/10230/53145 http://dx.doi.org/10.1007/s42979-021-00952-7 |
| Access Level: | acceso abierto |
| Palabra clave: | Multi-domain term collection and bilingual terminologies MCR-based terminologies |
| Sumario: | 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. |
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