Project Details
Description
Our collaboration on the Superlative Object Construction (SOC) in Late Modern American English combines corpus data with experimentation to fill a gap in synchronic and diachronic research on English resultative constructions. The case study on SOCs, as in They worked their hardest, contributes to ongoing modelling of transitivity changing constructions in diachronic construction grammar and discussions on constructional links in a sub-part of the construct-i-con (i.e. non-ordinary / manner resultatives). Specifically, we will use a bottom-up approach to retrieve SOCs from a corpus of Late Modern American texts to investigate the range of verbs attested in the construction, its internal variability, and development with respect to the degree of transitivity, measured on the basis of a range of parameters. In addition to this, we will apply Variability-based Neighbour Clustering to arrive at a data-driven periodisation of our data and run several co-varying collexeme analyses to model the semantic properties of the SOC. The results will then be compared against those previously observed in the development of analogous resultative constructions, namely the Cognate Object and the Reaction Object construction. For the comparison between the former and the SOC, additional data will be gathered and a distinctive collexeme analysis will be performed. In a follow-up study, we will use the insights from the case study just outlined to design an online experiment that will allow us to model the strength of the constructional links in this family of constructions. The findings of the corpus-based case study and the experiment will be submitted to peer-reviewed journals in the field. We expect our collaboration to further advance discussions on the recent debates revolving around the modelling of links in constructional networks and also on the important changes that affected English argument structure constructions since Old English times.
Status | Finished |
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Effective start/end date | 15/08/02 → 30/06/23 |
Funding
- National Science Foundation: $220,001.00