Automatic Extraction of Constructional Schemas

In this research, we use machine learning techniques to provide solutions for descriptive linguists in the domain of language standardization. With regard to the personal name construction in Afrikaans, we perform function learning from word pairs using the Default&Refine algorithm. We demonstrate how the extracted rules can be used to identify irregularities in previously standardized constructions and to predict new forms of unseen words. In addition, we define a generic, automated process that allows us to extract constructional schemas and present these visually as categorization networks, similar to what is often being used in Cognitive Grammar. We conclude that computational modeling of constructions can contribute to new descriptive linguistic insights, and to practical language solutions.