When GL meets the corpus: a data-driven investigation of lexical types and coercion phenomena

In this paper we present an analysis of corpus-derived V-arg combinations aiming to provide a datadriven characterization of Lexical Types (LTs) and represent how types behave compositionally, i.e. how they enter compositional processes and are modulated by them. We will do so using the enriched compositional rules and the type system as presented in Pustejovsky (2006). Our main concerns are twofold: i.) first of all, we want to show with a specific case-study (§. 5 onwards) how a data-driven investigation can shed light on the structure and the combinatorics of LTs; ii.) starting from the results of this investigation, we intend to propose a general methodology for lexical modeling in which the Generative Lexicon (GL) theory and corpus analysis are deeply interwoven in a process of mutual feeding. In fact, we argue that, if on the one hand corpus data can help to anchor the study of lexical dynamics and type system on empirical evidence, on the other hand GL can provide the crucial interpretative key for corpus data.