Comparing and harmonizing different verb classifications in light of a semantic annotation task

Abstract The verb lexicon can be classified in different and complementary ways; each approach faces challenges and limits. We focus on two large-scale lexical resources, WordNet (Miller et al. 1990; Fellbaum 1998) and FrameNet (Baker et al. 2003). WordNet is a semantic network where lexical meaning is represented in terms of relations among word forms. In contrast to WordNet's paradigmatically organized entries, FrameNet's Lexical Units are embedded in corpus-derived contexts, which serve as a basis for semantic distinctions. The classificatory perspective that each resource contributes to the analysis of the verb lexicon is illustrated with specific examples. Constructing verb typologies, even when based on attested data, requires the lexicographer's introspection and judgments, and no two classifications are completely alike. In a case-based approach to compare and harmonize WordNet and FrameNet, we ask human annotators to select the context-appropriate senses from each resources that best fit tokens in a corpus. A typology of alignments is proposed.