Data-to-text generation for domain-specific purposes

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dc.citation.epage61
dc.citation.journalTitleComputational Linguistics and Intelligent Systems
dc.citation.spage60
dc.citation.volume2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019
dc.contributor.affiliationTaras Shevchenko National University of Kyiv
dc.contributor.authorDrobot, Tetiana
dc.coverage.placenameLviv
dc.date.accessioned2019-10-31T13:21:00Z
dc.date.available2019-10-31T13:21:00Z
dc.date.created2019-04-18
dc.date.issued2019-04-18
dc.description.abstractThe first commercial implementation of Natural Language Generation (NLG) system dates back to the turn of the XXI century. Since then two main methods of NLG – text-to-text generation and data-to-text generation – have grown more complex in order to solve new business challenges. This research project focuses on the full cycle of template-based generation of hotel descriptions from linguistic and non-linguistic input: starting with data scraping and preparation up to rendering the whole text. Also, several improvements to the template- based approach were suggested.
dc.format.extent60-61
dc.format.pages2
dc.identifier.citationDrobot T. Data-to-text generation for domain-specific purposes / Tetiana Drobot // Computational Linguistics and Intelligent Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 60–61. — (Student section).
dc.identifier.citationenDrobot T. Data-to-text generation for domain-specific purposes / Tetiana Drobot // Computational Linguistics and Intelligent Systems. — Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 60–61. — (Student section).
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45483
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Linguistics and Intelligent Systems (2), 2019
dc.relation.referencesen1. Gatt, A., Krahmer, E.: Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61, pp. 65–170 (2018)
dc.relation.referencesen2. Reiter, E., Dale, R.: Building natural language generation systems. Cambridge University Press, Cambridge, UK (2000)
dc.relation.referencesen3. Learning to tell tales: automatic story generation from Corpora, https://urlzs.com/GUcj. Last accessed 10 Apr 2019
dc.relation.urihttps://urlzs.com/GUcj
dc.rights.holder© 2019 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectNatural Language Generation
dc.subjectdata-to-text generation
dc.subjecttemplate-based approach
dc.titleData-to-text generation for domain-specific purposes
dc.typeArticle

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