Defining Crime: A multifaceted approach based on Lexicographic Relevance and Distributional Semantics

This paper demonstrates how the parallel examination of distributional data and frame semantic information can expose word senses that are not documented in FrameNet. In our case study, we compare the distributional features of the word crime to its properties stored in the FrameNet database also considering dictionary data that we find in three online monolingual dictionaries. Our analysis indicates that crime has senses that are absent from FrameNet. The five senses that we identify can be separated on the basis of (a) frame hierarchies, (b) frame elements, (c) syntactic and semantic data extracted from corpora using lexicographical tools and (d) distributional similarity. Annotated examples are provided to demonstrate each sense.