Lightweight Parsing of Natural Language Metadata

Understanding metadata written in natural language is a premise to successful automated integration of large scale language-rich datasets, such as digital libraries. In this paper we describe an analysis of the part of speech structure of two different datasets of metadata, show how this structure can be used to detect structural patterns that can be parsed by lightweight grammars with an accuracy ranging from 95.3% to 99.8%. This allows deeper understanding of metadata semantics, important for such tasks as translating classifications into lightweight ontologies for use in semantic matching.