A Broad-Coverage Deep Semantic Lexicon for Verbs
This addresses the bottleneck of lacking comprehensive lexical resources for deep language understanding in NLP, though it is incremental as it builds on existing hand-built lexicons and ontologies.
The researchers tackled the problem of limited deep language understanding by creating COLLIE-V, a broad-coverage deep semantic lexicon for verbs that matches WordNet's coverage while providing superior syntactic and semantic details, achieving high accuracy in automatically deriving new concepts and lexical entries.
Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.