ASC analyzer: A Python package for measuring argument structure construction usage in English texts
This provides a scalable tool for assessing second language proficiency, though it is incremental as it builds on existing theoretical frameworks.
The paper introduces the ASC analyzer, a Python package that automatically tags argument structure constructions (ASCs) and computes 50 indices to measure their usage in English texts, and it demonstrates utility by analyzing relationships between these indices and L2 writing scores.
Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores.