A Hybrid Intelligence Method for Argument Mining

Journal of Artificial Intelligence Research, vol. 80, pp. 1187–1222

Abstract

Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs 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 citizen 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 to 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 artificial intelligence.

Authors 6

1. Michiel van der Meer Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS)

2. Enrico Liscio

   Delft University of Technology

   Affiliation as printed

   TU Delft

3. Catholijn M. Jonker

   Delft University of Technology

   Affiliation as printed

   TU Delft

4. Aske Plaat Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science

5. Piek T. J. M. Vossen

   Vrije Universiteit Amsterdam

   Affiliation as printed

   Vrije Universiteit Amsterdam

6. Pradeep Kumar Murukannaiah

   Delft University of Technology

   Affiliation as printed

   TU Delft