Modeling crosslinguistic diversity in Differential Object Marking through the Parametric Comparison Method
Project The present project aims at designing a system of syntactic parameters, shaped on the toolkit
provided by the Parametric Comparison Method (PCM), in order to model crosslinguistic variation in
a domain of the grammar which has never been explored at this broad level, namely that of
Differential Object Marking (DOM). To achieve this goal, statistical techniques will be instrumental.
Despite the pervasiveness of splits in the morpho-syntactic marking of objects, which are at the core
of DOM, and despite deep typological and formal investigation, there is no current unified theoretical
framework this phenomenon can be systematized under. This project aims at taking the first steps in
filling this gap. In doing so, not only will it contribute to the understanding of the formal nature of
objects, but it will also enhance insights into the cognitive mechanisms underlying syntactic diversity
across natural languages.
This unprecedented enterprise is now conceivable thanks to the formal, methodological framework
made available by the PCM, which in this project will be applied to a radically novel set of data.
The project has two main goals:
1. Deriving from a constrained set of parameters the apparently unrestrained distribution of
crosslinguistic DOM manifestations
2. Providing a novel testing domain for modeling and measuring crosslinguistic syntactic diversity
through the PCM
In the model adopted by the PCM, each parameter is minimally defined by the following properties:
(i) it stems from a universal set of more abstract formats; (ii) it is responsible of different (covarying)
observable patterns; (iii) it is part of a network of cross-parametric dependencies.
Adapting the domain of variation encompassed by DOM to such a rigorous system poses non-trivial
challenges, due to the vast amount of interacting properties in the manifestations of this
phenomenon. This will be addressed with the crucial aid of statistical investigation. Different bivariate
and multivariate methods will be applied, such as cluster analysis, multivariate correspondence
analysis and association between pairs of languages.
The initial dataset covers 60 languages from various families, mostly from Eurasia (Indo-European,
Basque, Sino-Tibetan, Altaic), but also including a significant sample of Afro-Asiatic. A
comprehensive list of DOM patterns will be created, starting from the material collected in Irimia’s
work, and considering complex interactions between DOM and various modules of the grammar
(e.g., the internal structure of the nominal domain, the verbal and clausal domain, tense and aspect).
The output of the project is a system of parameters defining the observed variation concerning DOM
in the dataset, which will eventually be applied at a wide-scale (ideally global) level. The parameters
will be set in the languages of the dataset, to test their consistency with the parameter setting
algorithm assumed in the model and with their explanatory power in terms of crosslinguistic
coverage. The parameter grid so obtained will then be used for measuring language similarity.