From Modal to Multimodal Ambiguities: a Classification Approach
This work addresses the challenge of ambiguity classification for multimodal interaction, which is incremental as it builds on existing literature for natural and visual languages.
The paper tackled the problem of classifying ambiguities in multimodal languages by developing an original classification that distinguishes between semantic and syntactic multimodal ambiguities, achieving classification accuracies of 94.6% for semantic and 92.1% for syntactic ambiguities compared to human judgment.
This paper deals with classifying ambiguities for Multimodal Languages. It evolves the classifications and the methods of the literature on ambiguities for Natural Language and Visual Language, empirically defining an original classification of ambiguities for multimodal interaction using a linguistic perspective. This classification distinguishes between Semantic and Syntactic multimodal ambiguities and their subclasses, which are intercepted using a rule-based method implemented in a software module. The experimental results have achieved an accuracy of the obtained classification compared to the expected one, which are defined by the human judgment, of 94.6% for the semantic ambiguities classes, and 92.1% for the syntactic ambiguities classes.