DeepAI
Log In Sign Up

From Modal to Multimodal Ambiguities: a Classification Approach

04/04/2017
by   Maria Chiara Caschera, et al.
0

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 semantic ambiguities classes, and 92.1

READ FULL TEXT
03/25/2017

Learning to Predict: A Fast Re-constructive Method to Generate Multimodal Embeddings

Integrating visual and linguistic information into a single multimodal r...
08/14/2020

A Multimodal Late Fusion Model for E-Commerce Product Classification

The cataloging of product listings is a fundamental problem for most e-c...
09/10/2021

Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining

Natural language relies on a finite lexicon to express an unbounded set ...
10/01/2020

Linguistic Structure Guided Context Modeling for Referring Image Segmentation

Referring image segmentation aims to predict the foreground mask of the ...
02/24/2021

Automatic Meter Classification of Kurdish Poems

Most of the classic texts in Kurdish literature are poems. Knowing the m...
12/02/2020

Classification of Multimodal Hate Speech – The Winning Solution of Hateful Memes Challenge

Hateful Memes is a new challenge set for multimodal classification, focu...
05/02/2020

Benchmarking Multimodal Regex Synthesis with Complex Structures

Existing datasets for regular expression (regex) generation from natural...