'Scuola Normale Superiore - Edizioni della Normale'
Abstract
Optionally transitive verbs, whose Patient participant is semantically obligatory but
syntactically optional (e.g., to eat, to drink, to write), deviate from the transitive prototype
defined by Hopper and Thompson (1980). Following Fillmore (1986), unexpressed objects
may be either indefinite (referring to prototypical Patients of a verb, whose actual entity
is unknown or irrelevant) or definite (with a referent available in the immediate intra- or
extra-linguistic context). This thesis centered on indefinite null objects, which the literature
argues to be a gradient, non-categorical phenomenon possible with virtually any transitive
verb (in different degrees depending on the verb semantics), favored or hindered by several
semantic, aspectual, pragmatic, and discourse factors. In particular, the probabilistic
model of the grammaticality of indefinite null objects hereby discussed takes into account
a continuous factor (semantic selectivity, as a proxy to object recoverability) and four
binary factors (telicity, perfectivity, iterativity, and manner specification).
This work was inspired by Medina (2007), who modeled the effect of three predictors
(semantic selectivity, telicity, and perfectivity) on the grammaticality of indefinite null
objects (as gauged via Likert-scale acceptability judgments elicited from native speakers
of English) within the framework of Stochastic Optimality Theory. In her variant of the
framework, the constraints get floating rankings based on the input verb’s semantic
selectivity, which she modeled via the Selectional Preference Strength measure by Resnik
(1993, 1996). I expanded Medina’s model by modeling implicit indefinite objects in two
languages (English and Italian), by using three different measures of semantic selectivity
(Resnik’s SPS; Behavioral PISA, inspired by Medina’s Object Similarity measure; and
Computational PISA, a novel similarity-based measure by Cappelli and Lenci (2020)
based on distributional semantics), and by adding iterativity and manner specification as
new predictors in the model.
Both the English and the Italian five-predictor models based on Behavioral PISA explain
almost half of the variance in the data, improving on the Medina-like three-predictor
models based on Resnik’s SPS. Moreover, they have a comparable range of predicted
object-dropping probabilities (30-100% in English, 30-90% in Italian), and the predictors
perform consistently with theoretical literature on object drop. Indeed, in both models,
atelic imperfective iterative manner-specified inputs are the most likely to drop their
object (between 80% and 90%), while telic perfective non-iterative manner-unspecified
inputs are the least likely (between 30% and 40%). The constraint re-ranking probabilities
are always directly proportional to semantic selectivity, with the exception of Telic End
in Italian. Both models show a main effect of telicity, but the second most relevant factor
in the model is perfectivity in English and manner specification in Italian