DAMA: A Dynamic Classification of Multimodal Ambiguities

DOI

10.2991/ijcis.d.200208.001How to use a DOI?

Keywords

Hidden Markov models; Human–machine interaction; Multimodal interaction; Natural language processing

Abstract

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.

Copyright

© 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/).

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TY  - JOUR
AU  - Patrizia Grifoni
AU  - Maria Chiara Caschera
AU  - Fernando Ferri
PY  - 2020
DA  - 2020/02/14
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  - 10.2991/ijcis.d.200208.001
ID  - Grifoni2020
ER  -