Edinburgh Research Explorer Category-Driven Content Selection Citation for published version: Mohammed, R, Perez-Beltrachini, L & Gardent, C 2016, Category-Driven Content Selection. in Proceedings of The 9th International Natural Language Generation conference . Association for Computational Linguistics, pp. 94-98, 9th International Natural Language Generation conference , Edinburgh, United Kingdom, 5/09/16. https://doi.org/10.18653/v1/W16-6616 Digital Object Identifier (DOI): 10.18653/v1/W16-6616 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Proceedings of The 9th International Natural Language Generation conference General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact
[email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 01. Oct. 2021 Category-Driven Content Selection Rania Mohamed Sayed Laura Perez-Beltrachini Claire Gardent Universite´ de Lorraine CNRS/LORIA CNRS/LORIA Nancy (France) Nancy (France) Nancy (France)
[email protected] [email protected] [email protected] Abstract or monuments). We introduce a content selec- tion method which, given an entity, retrieves from In this paper, we introduce a content selection DBPedia an RDF subgraph that encodes relevant method where the communicative goal is to describe entities of different categories (e.g., and coherent knowledge about this entity.