Modeling the partial productivity of constructions

People regularly produce novel sentences that sound nativelike (e.g., she googled us the information), while they also recognize that other novel sentences sound odd, even though they are interpretable (e.g., ? She explained us the information). This work offers a Bayesian, incremental model that learns clusters that correspond to grammatical constructions of different type and token frequencies. Without specifying in advance the number of constructions, their semantic contributions, nor whether any two constructions compete with one another, the model successfully generalizes when appropriate while identifying and suggesting an alternative when faced with overgeneralization errors. Results are consistent with recent psycholinguistic work that demonstrates that the existence of competing alternatives and the frequencies of those alternatives play a key role in the partial productivity of grammatical constructions. The model also goes beyond the psycholinguistic work in that it investigates a role for constructions' overall frequency.