HyEnA: A Hybrid Method for Extracting Arguments from Opinions
Frontiers in artificial intelligence and applications
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.
Authors 6
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Affiliation as printed
Leiden Institute for Advanced Computer Science (LIACS), Leiden University
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Delft University of Technology
Affiliation as printed
Interactive Intelligence (II), TU Delft
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Leiden University · Delft University of Technology
Affiliation as printed
Interactive Intelligence (II), TU Delft
Leiden Institute for Advanced Computer Science (LIACS), Leiden University
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Affiliation as printed
Leiden Institute for Advanced Computer Science (LIACS), Leiden University
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Affiliation as printed
Computational Lexicology and Terminology Lab (CLTL), VU
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Delft University of Technology
Affiliation as printed
Interactive Intelligence (II), TU Delft
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