Modelling Semantic Association and Conceptual Inheritance for Semantic Analysis

TL;DR

This paper presents a method for interpreting sequences of icons as complex messages by reconstructing conceptual relations, enabling natural language generation for language learning systems.

Contribution

It introduces a novel approach to interpret icon sequences using semantic information to build conceptual graphs for improved communication.

Findings

Effective interpretation of icon sequences achieved

Conceptual graphs successfully represent meaning

Enables natural language sentence generation

Abstract

Allowing users to interact through language borders is an interesting challenge for information technology. For the purpose of a computer assisted language learning system, we have chosen icons for representing meaning on the input interface, since icons do not depend on a particular language. However, a key limitation of this type of communication is the expression of articulated ideas instead of isolated concepts. We propose a method to interpret sequences of icons as complex messages by reconstructing the relations between concepts, so as to build conceptual graphs able to represent meaning and to be used for natural language sentence generation. This method is based on an electronic dictionary containing semantic information.