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Using ontologies to interlink linguistic annotations and improve their accuracy

TitleUsing ontologies to interlink linguistic annotations and improve their accuracy
Publication TypeBook Chapter
Year of Publication2016
AuthorsPareja-Lora, A
EditorPareja-Lora, A, Calle-Martínez, C, Rodríguez-Arancón, P
Book TitleNew perspectives on teaching and working with languages in the digital era

For the new approaches to language e-learning (e.g. language blended learning, language autonomous learning or mobile-assisted language learning) to succeed, some automatic functions for error correction (for instance, in exercises) will have to be included in the long run in the corresponding environments and/or applications. A possible way to achieve
this is to use some Natural Language Processing (NLP) functions within language e-learning applications. These functions should be based on some truly reliable and wide-coverage linguistic annotation tools (e.g. a Part-Of-Speech (POS) tagger, a syntactic parser and/or a semantic tagger). However, linguistic annotation tools usually introduce a not insignificant rate of errors and ambiguities when tagging, which prevents them from being used ‘as is’ for this purpose. In this paper, we present an annotation architecture and methodology that has helped reduce the rate of errors in POS tagging, by making several POS taggers interoperate and supplement each other. We also introduce briefly the set of ontologies that have helped all these tools intercommunicate and collaborate in order to produce a more accurate joint POS tagging, and how these ontologies were used towards this end. The resulting POS tagging error rate is around 6%, which should allow this function to be included in language e-learning applications for the purpose aforementioned.