Semi-supervised algorithm for concept ontology based word set expansion

Abstract

Word lists that contain closely related sets of words is a critical requirement in machine understanding and processing of natural languages. Creating and maintaining such closely related word lists is a critical and complex process that requires human input and carried out manually in the absence of tools. We describe a supervised learning mechanism which employs a word ontology to expand word lists containing closely related sets of words. The approach described in this paper uses two novel supervised learning techniques that complement each other for the purpose of expanding existing lists of related words. Expanding concept variable lists of RelEx2Frame component of OpenCog Artificial General Intelligence Framework using WordNet is used as a proof of concept. Intervention of this project would enable OpenCog applications to attempt to understand words that they were not able to understand before, due to the limited size of existing lists of related words.