Harmonizing WordNet and FrameNet

Lexical semantic resources are a key component of many NLP systems, whose performance continues to be limited by the "lexical bottleneck". Two large hand-constructed resources, WordNet and FrameNet, differ in their theoretical foundations and their approaches to the representation of word meaning. A core question that both resources address is, how can regularities in the lexicon be discovered and encoded in a way that allows both human annotators and machines to better discriminate and interpret word meanings? WordNet organizes the bulk of the English lexicon into a network (an acyclic graph) of word form-meaning pairs that are interconnected via directed arcs that express paradigmatic semantic relations. This classification largely disregards syntagmatic properties such as argument selection for verbs. However, a comparison with a syntax-based approach like Levin (1993) reveals some overlap as well as systematic divergences that can be straightforwardly ascribed to the different classification principles. FrameNet's units are cognitive schemas (Frames), each characterized by a set of lexemes from different parts of speech with Frame-specific meanings (lexial units) and roles (Frame Elements). FrameNet also encodes cross-frame relations that parallel the relations among WordNet's synsets.