DAMA: A Dynamic Classification of Multimodal Ambiguities
- https://doi.org/10.2991/ijcis.d.200208.001How to use a DOI?
- Hidden Markov models, Human–machine interaction, Multimodal interaction, Natural language processing
Ambiguities represent uncertainty but also a fundamental item of discussion for who is interested in the interpretation of languages and it is actually functional for communicative purposes both in human–human communication and in human–machine interaction. This paper faces the need to address ambiguity issues in human–machine interaction. It deals with the identification of the meaningful features of multimodal ambiguities and proposes a dynamic classification method that characterizes them by learning, and progressively adapting with the evolution of the interaction language, by refining the existing classes, or by identifying new ones. A new class of ambiguities can be added by identifying and validating the meaningful features that characterize and distinguish it compared to the existing ones. The experimental results demonstrate improvement in the classification rate over considering new ambiguity classes.
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Patrizia Grifoni AU - Maria Chiara Caschera AU - Fernando Ferri PY - 2020 DA - 2020/02 TI - DAMA: A Dynamic Classification of Multimodal Ambiguities JO - International Journal of Computational Intelligence Systems SP - 178 EP - 192 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200208.001 DO - https://doi.org/10.2991/ijcis.d.200208.001 ID - Grifoni2020 ER -