HyEnA
A Hybrid Method for Extracting Arguments from Opinions
M.T. van der Meer (TU Delft - Interactive Intelligence, Universiteit Leiden)
E. Liscio (TU Delft - Interactive Intelligence)
C.M. Jonker (Universiteit Leiden, TU Delft - Interactive Intelligence)
Aske Plaat (Universiteit Leiden)
Piek Vossen (Computational Lexicology and Terminology Lab (CLTL))
Pradeep Kumar Murukannaiah (TU Delft - Interactive Intelligence)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.
help_outline