Print Email Facebook Twitter HyEnA Title HyEnA: A Hybrid Method for Extracting Arguments from Opinions Author van der Meer, M.T. (TU Delft Interactive Intelligence; Universiteit Leiden) Liscio, E. (TU Delft Interactive Intelligence) Jonker, C.M. (TU Delft Interactive Intelligence; Universiteit Leiden) Plaat, Aske (Universiteit Leiden) Vossen, Piek (Computational Lexicology and Terminology Lab (CLTL)) Murukannaiah, P.K. (TU Delft Interactive Intelligence) Contributor Schlobach, Stefan (editor) Perez-Ortiz, Maria (editor) Tielman, Myrthe (editor) Date 2022 Abstract The key arguments underlying a large and noisy set of opinions help understand the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method, when compared on a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and machine intelligence. Subject argument extractionhybrid intelligencenatural language processing To reference this document use: http://resolver.tudelft.nl/uuid:7b334e72-1230-4dbe-8973-d1facf647b9b DOI https://doi.org/10.3233/FAIA220187 Publisher IOS Press ISBN 9781643683089 Source HHAI2022: Augmenting Human Intellect - Proceedings of the 1st International Conference on Hybrid Human-Artificial Intelligence Event 1st International Conference on Hybrid Human-Artificial Intelligence, HHAI 2022, 2022-06-13 → 2022-06-17, Amsterdam, Netherlands Series Frontiers in Artificial Intelligence and Applications, 0922-6389, 354 Part of collection Institutional Repository Document type conference paper Rights © 2022 M.T. van der Meer, E. Liscio, C.M. Jonker, Aske Plaat, Piek Vossen, P.K. Murukannaiah Files PDF FAIA_354_FAIA220187.pdf 442.3 KB Close viewer /islandora/object/uuid:7b334e72-1230-4dbe-8973-d1facf647b9b/datastream/OBJ/view