NeuralREG: An end-to-end approach to referring expression generation

Autor: Ferreira, Thiago Castro, Moussallem, Diego, Kádár, Ákos, Wubben, Sander, Krahmer, Emiel

Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we prese

Externí odkaz: http://arxiv.org/abs/1805.08093

Creating a reference data set for the summarization of discussion forum threads

Autor: Verberne, Suzan, Krahmer, Emiel, Hendrickx, Iris, Wubben, Sander, van den Bosch, Antal

Publikováno v: Language Resources and Evaluation, 2018 Apr 01. 52(2), 461-483.

Externí odkaz: https://www.jstor.org/stable/26649734

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Examining Popularity Predictors in Visual Brand-related User Generated Content: A Machine Learning Approach

Autor: Nanne, Annemarie, Antheunis, Marjolijn, Wubben, Sander, Postma, Eric, van Noort, Guda

Publikováno v: ISSUE=1;TITLE=Interactive Marketing Research Conference

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::15e4d72577313011d4caee6dd7c60178
https://research.tilburguniversity.edu/en/publications/12ebe825-9c3a-416f-a28a-b8d104de4333

Aspect-based summarization of pros and cons in unstructured product reviews

Autor: Kunneman, Florian, Wubben, Sander, van den Bosch, Antal, Krahmer, Emiel

Publikováno v: Proceedings of the 27th International Conference on Computational Linguistics, 2219-2229
STARTPAGE=2219;ENDPAGE=2229;TITLE=Proceedings of the 27th International Conference on Computational Linguistics

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::f50fc50a98a2bc39f7e45804a8b72be4
https://research.tilburguniversity.edu/en/publications/109e5bbc-e69c-49d0-956e-f821f9eb210d