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pro vyhledávání: '"Uma, Alexandra"'
Autor:
Leonardelli, Elisa, Uma, Alexandra, Abercrombie, Gavin, Almanea, Dina, Basile, Valerio, Fornaciari, Tommaso, Plank, Barbara, Rieser, Verena, Poesio, Massimo
NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter c
Externí odkaz:
http://arxiv.org/abs/2304.14803
Autor:
Uma, Alexandra N., Sityaev, Dmitry
This paper provides results of evaluating some text summarisation techniques for the purpose of producing call summaries for contact centre solutions. We specifically focus on extractive summarisation methods, as they do not require any labelled data
Externí odkaz:
http://arxiv.org/abs/2209.02472
Anaphora resolution (coreference) systems designed for the CONLL 2012 dataset typically cannot handle key aspects of the full anaphora resolution task such as the identification of singletons and of certain types of non-referring expressions (e.g., e
Externí odkaz:
http://arxiv.org/abs/1911.09532
Akademický článek
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Autor:
Uma A; Computational Linguistics Lab, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom., Almanea D; Computational Linguistics Lab, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom., Poesio M; Computational Linguistics Lab, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.; Digital Environment Research Institute, Queen Mary University of London, London, United Kingdom.; Turing Institute, London, United Kingdom.
Publikováno v:
Frontiers in artificial intelligence [Front Artif Intell] 2022 Apr 01; Vol. 5, pp. 818451. Date of Electronic Publication: 2022 Apr 01 (Print Publication: 2022).
