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A constructicon, i.e., a structured inventory of constructions, essentially aims at documenting functions of lexical and grammatical constructions. Among other parameters, so-called constructional collo-profiles, as introduced by Herbst (2018, 2020), are conclusive for determining constructional meanings. They provide information on how relevant individual words are for construction slots, they hint at usage preferences of constructions and serve as a helpful indicator for semantic peculiarities of constructions. However, even though collo-profiles constitute an indispensable component of constructicon entries, they pose major challengers for constructicographers: For a constructicographic enterprise it is not feasible to conduct collostructional analyses for hundreds or even thousands of constructions. In this article, we introduce a procedure based on the large language model BERT that allows to predict collo-profiles without having to extensively annotate instances of constructions in a given corpus. Specifically, by discussing the constructions X macht Y ADJP (‘x makes Y ADJ’, e.g. he drives him crazy) and N1 PREP N1 (e.g., bumper to bumper, constructions over constructions), we show how the developed automated system generates collo-profiles based on a limited number of annotated instances. Finally, we place collo-profiles alongside other dimensions of constructional meanings included in the German Constructicon.
Construction-based language models assume that grammar is meaningful and learnable from experience. Focusing on five of the most elementary argument structure constructions of English, a large-scale corpus study of child-directed speech (CDS) investigates exactly which meanings/functions are associated with these patterns in CDS, and whether they are indeed specially indicated to children by their caretakers (as suggested by previous research, cf. Goldberg, Casenhiser and Sethuraman 2004). Collostructional analysis (Stefanowitsch and Gries 2003) is employed to uncover significantly attracted verb-construction combinations, and attracted pairs are classified semantically in order to systematise the attested usage patterns of the target constructions. The results indicate that the structure of the input may aid learners in making the right generalisations about constructional usage patterns, but such scaffolding is not strictly necessary for construction learning: not all argument structure constructions are coherently semanticised to the same extent (in the sense that they designate a single schematic event type of the kind envisioned in Goldberg’s [1995] ‘scene encoding hypothesis’), and they also differ in the extent to which individual semantic subtypes predominate in learners’ input