Gradient grammar: An effect of animacy on the syntax of give in New Zealand and American English

Abstract

Bresnan et al. (2007) show that a statistical model can predict United States (US) English speakers' syntactic choices with 'give'-type verbs extremely accurately. They argue that these results are consistent with probabilistic models of grammar, which assume that grammar is quantitive, and learned from exposure to other speakers. Such a model would also predict syntactic differences across time and space which are reflected not only in the use of clear dialectal features or clear-cut changes in progress, but also in subtle factors such as the relative importance of conditioning factors, and changes over time in speakers' preferences between equally well-formed variants. This paper investigates these predictions by comparing the grammar of phrases involving 'give' in New Zealand (NZ) and US English. We find that the grammar developed by Bresnan et al. for US English generalizes remarkably well to NZ English. NZ English is, however, subtly different, in that NZ English speakers appear to be more sensitive to the role of animacy. Further, we investigate changes over time in NZ English and find that the overall behavior of 'give' phrases has subtly shifted. We argue that these subtle differences in space and time provide support for the gradient nature of grammar, and are consistent with usage-based, probabilistic syntactic models.

Where to place inaccessible subjects in Dutch: The role of definiteness and animacy

“…Our findings are also compatible with a probabilistic account of syntactic variation, in which multiple constraints simultaneously affect the likelihood that a constituent is placed in a certain position (e.g. Bates and MacWhinney 1989;Bresnan and Hay 2008;Bresnan et al 2007;Gries 2003). In this paper, we have focused on the interplay between definiteness and animacy.…”

Spoken syntax in a comparative perspective: The dative and genitive alternation in varieties of English

“…Bresnan and Hay (2008) find that non-animate recipients are more likely to be used in the prepositional dative construction in spoken NZE than in spoken AmE (Bresnan & Hay 2008: Figure 1); Wolk et al (2013) report that end-weight of themes has a stronger effect in (written) AmE than in (written) BrE; Tagliamonte (2014) diagnoses greater use of the ditransitive dative variant in BrE as well as a greater degree of spread of the ditransitive dative variant into animate recipients in contrast to CanE where the prepositional dative variant is fairly stable. Finally, with a regression model comparing each of nine varieties (including BrE, CanE and NZE) to a collective "average" variety, Röthlisberger et al (to appear) show, among other things, that non-pronominal recipients favor the prepositional dative significantly more so in CanE than in other varieties.…”

“…The observations come from the Origins of New Zealand English corpora (ONZE) (see Gordon, Maclagan & Hay 2007), and were collected separately but overlap substantially with those presented by Bresnan and Hay (2008). Note that the New Zealand dative sub-dataset includes historical data from early New Zealand English speakers, and so covers speakers born between 1851 and 1984 (N = 845).…”

Predicting head-marking variability in Yucatec Maya relative clause production

“…On this view, the production behavior in our experiment would be the outcome of speakers' fi ne-grained probabilistic knowledge of the linguistic conventions of their language: speakers have learned from exposure to their speech community that reduced verb forms are more frequent in relative clauses that are headed with defi nite NPs than with indefi nite NPs, and they recapitulate these distributional patterns in their own productions. There is, indeed, thought-provoking evidence that on-line pressures alone are insuffi cient to explain patterns across varieties of English, and thus that variability in production may sometimes refl ect probabilistic linguistic knowledge (Bresnan & Hay, 2007 ;Rosenbach, 2002Rosenbach, , 2003. Such knowledge could take the form of a probabilistic grammar (where probabilities are associated with conventional rules or constraints; see, e.g., Hale, 2003 ;Manning, 2003 ), or, as in the case of exemplar-based models, could consist of stored chunks of previously experienced language (Bod, 1998 ).…”