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.
@article{arxiv.2510.10384,
title = {ASC analyzer: A Python package for measuring argument structure construction usage in English texts},
author = {Hakyung Sung and Kristopher Kyle},
journal= {arXiv preprint arXiv:2510.10384},
year = {2025}
}
Comments
Accepted to the 2nd Workshop on Construction Grammars and NLP (CxGs+NLP)