Joint learning of ontology and semantic parser from text
This work addresses the challenge of incomplete meaning representations in semantic parsing by enabling joint learning, which could benefit natural language processing applications, though it appears incremental in combining existing techniques.
The paper tackles the problem of jointly learning an ontology and a semantic parser from text, using a semi-automatic grammar induction method to parse text into semantic trees and construct ontologies. The approach was evaluated on Wikipedia sentences about people, achieving results that demonstrate its feasibility.
Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar and the semantic trees are used to learn the ontology on several levels -- classes, instances, taxonomic and non-taxonomic relations. The approach was evaluated on the first sentences of Wikipedia pages describing people.